Tootfinder

Differentially Private Verification of Survey-Weighted Estimates
Tong Lin, Jerome P. Reiter
https://arxiv.org/abs/2404.02519 https://…

Differentially Private Verification of Survey-Weighted Estimates
Several official statistics agencies release synthetic data as public use microdata files. In practice, synthetic data do not admit accurate results for every analysis. Thus, it is beneficial for agencies to provide users with feedback on the quality of their analyses of the synthetic data. One approach is to couple synthetic data with a verification server that provides users with measures of the similarity of estimates computed with the synthetic and underlying confidential data. However, suc…

"Google LLC on Monday agreed to delete billions of data records that reflect certified class members' private browsing activities as part of a nonmonetary eve-of-trial settlement to resolve allegations that the tech giant surreptitiously tracks Chrome users running the browser's incognito mode."
In the meantime, of course, they used that data to train their LLMs. So the violation persists inside the LLM and is available for further discrimination.

Google Agrees To Delete Data To End 'Incognito' Class Claims - Law360
Google LLC on Monday agreed to delete billions of data records that reflect certified class members' private browsing activities as part of a nonmonetary eve-of-trial settlement to resolve allegations that the tech giant surreptitiously tracks Chrome users running the browser's incognito mode.

This https://arxiv.org/abs/2401.15502 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Differentially private Bayesian tests
Differential privacy has emerged as an significant cornerstone in the realm of scientific hypothesis testing utilizing confidential data. In reporting scientific discoveries, Bayesian tests are widely adopted since they effectively circumnavigate the key criticisms of P-values, namely, lack of interpretability and inability to quantify evidence in support of the competing hypotheses. We present a novel differentially private Bayesian hypotheses testing framework that arise naturally under a pri…

Sources: Microsoft and OpenAI are a planning a new "Stargate" US-based supercomputer that could cost $100B to power OpenAI products (The Information)
https://www.theinformation.com/articles/microsoft-and-openai-plot…

Microsoft and OpenAI Plot $100 Billion Stargate AI Supercomputer
Executives at Microsoft and OpenAI have been drawing up plans for a data center project that would contain a supercomputer with millions of specialized server chips to power OpenAI’s artificial intelligence, according to three people who have been involved in the private conversations about the ...

"Google LLC on Monday agreed to delete billions of data records that reflect certified class members' private browsing activities as part of a nonmonetary eve-of-trial settlement to resolve allegations that the tech giant surreptitiously tracks Chrome users running the browser's incognito mode."
In the meantime, of course, they used that data to train their LLMs. So the violation persists inside the LLM and is available for further discrimination.

Google Agrees To Delete Data To End 'Incognito' Class Claims - Law360
Google LLC on Monday agreed to delete billions of data records that reflect certified class members' private browsing activities as part of a nonmonetary eve-of-trial settlement to resolve allegations that the tech giant surreptitiously tracks Chrome users running the browser's incognito mode.

This https://arxiv.org/abs/2308.04455 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques
The growing use of voice user interfaces has led to a surge in the collection and storage of speech data. While data collection allows for the development of efficient tools powering most speech services, it also poses serious privacy issues for users as centralized storage makes private personal speech data vulnerable to cyber threats. With the increasing use of voice-based digital assistants like Amazon's Alexa, Google's Home, and Apple's Siri, and with the increasing ease with which personal…

THANK YOU CBC!!!
"Over the six-year period covered by the data, more than 1.5 million study permits were issued for students to attend some 1,300 colleges and universities.
That translates into international students paying tens of billions of dollars into Canada's post-secondary system — at a time when provincial governments were imposing austerity measures on public universities and colleges.”
INCLUDING RIGHT NOW! - VIU is making the largest cuts *In It's Hist…

This https://arxiv.org/abs/2404.17553 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Federated Transfer Component Analysis Towards Effective VNF Profiling
The increasing concerns of knowledge transfer and data privacy challenge the traditional gather-and-analyse paradigm in networks. Specifically, the intelligent orchestration of Virtual Network Functions (VNFs) requires understanding and profiling the resource consumption. However, profiling all kinds of VNFs is time-consuming. It is important to consider transferring the well-profiled VNF knowledge to other lack-profiled VNF types while keeping data private. To this end, this paper proposes a F…

I think as a public benefit, then the stealing of everyone's copyrighted data could be construed as fair use, whereas private entities violating copyright for profit (and then of course crying foul if anyone would steal *their* IP) is pretty hypocritical.

For the past ~2 years, I’ve been working on a neuroscience startup (called Matter). We have an app that—by its nature—needs to operate on private user data. Some of that data is so private that we never actually want to handle it ourselves. So we don’t.
We can’t leak data we don’t have. We can’t mishandle private info that never leaves the control of the user.
We’re taking a mostly-unique approach to user data at Matter, and I’ve finally written a little bit about it:

Matter and Privacy
When I was still working at Faculty, we took on a new client that was not yet named Matter. We eventually transitioned from an agency-client relationship to a startup relationship, where I became the

This https://arxiv.org/abs/2308.04455 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques
The growing use of voice user interfaces has led to a surge in the collection and storage of speech data. While data collection allows for the development of efficient tools powering most speech services, it also poses serious privacy issues for users as centralized storage makes private personal speech data vulnerable to cyber threats. With the increasing use of voice-based digital assistants like Amazon's Alexa, Google's Home, and Apple's Siri, and with the increasing ease with which personal…

SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery
Ajinkya Kiran Mulay, Xiaojun Lin
https://arxiv.org/abs/2402.19016

SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery
Sparse basis recovery is a classical and important statistical learning problem when the number of model dimensions $p$ is much larger than the number of samples $n$. However, there has been little work that studies sparse basis recovery in the Federated Learning (FL) setting, where the client data's differential privacy (DP) must also be simultaneously protected. In particular, the performance guarantees of existing DP-FL algorithms (such as DP-SGD) will degrade significantly when $p \gg n$, a…

Register by Friday 29 April for the 20th Special Interest Group on #Network Operations Centres (SIG-NOC) meeting in Helsinki, Finland, on the 7-8 May. Hosted by CSC/FUNET, topics will include #Data Lakes in Data Centres operations,

Registrations for the 20th SIG-NOC meeting close Friday 29 April | GÉANT CONNECT Online
The 20th SIG-NOC meeting will take place in Helsinki, Finland on the 7-8 May, kindly hosted by CSC/FUNET. The first day will discuss Data Lakes in Data Centres operations, Monitoring Object-Storage Services, Fibre sensing technologies and trials, and boundaries between SOCs and NOCs. In the evening there will be a private visit to the Elisa

THANK YOU CBC!!!
"Over the six-year period covered by the data, more than 1.5 million study permits were issued for students to attend some 1,300 colleges and universities.
That translates into international students paying tens of billions of dollars into Canada's post-secondary system — at a time when provincial governments were imposing austerity measures on public universities and colleges.”
INCLUDING RIGHT NOW! - VIU is making the largest cuts *In It's Hist…

AAA: an Adaptive Mechanism for Locally Differential Private Mean Estimation
Fei Wei, Ergute Bao, Xiaokui Xiao, Yin Yang, Bolin Ding
https://arxiv.org/abs/2404.01625

AAA: an Adaptive Mechanism for Locally Differential Private Mean Estimation
Local differential privacy (LDP) is a strong privacy standard that has been adopted by popular software systems. The main idea is that each individual perturbs their own data locally, and only submits the resulting noisy version to a data aggregator. Although much effort has been devoted to computing various types of aggregates and building machine learning applications under LDP, research on fundamental perturbation mechanisms has not achieved significant improvement in recent years. Towards a…

I swear programmers (me included) have become login-wall pilled.
I'm working on something that's data that is universal: public, the same no matter who's viewing it. And because of that I can just sync updates. If a file with a certain ID gets new data, it's because someone went and fixed or added data to it. And I can just ... add it to my copy of the collection. We're sharing.
after working on so much private stuff for work this feels very strange.

Tighter Bounds for Local Differentially Private Core Decomposition and Densest Subgraph
Monika Henzinger, A. R. Sricharan, Leqi Zhu
https://arxiv.org/abs/2402.18020

Tighter Bounds for Local Differentially Private Core Decomposition and Densest Subgraph
Computing the core decomposition of a graph is a fundamental problem that has recently been studied in the differentially private setting, motivated by practical applications in data mining. In particular, Dhulipala et al. [FOCS 2022] gave the first mechanism for approximate core decomposition in the challenging and practically relevant setting of local differential privacy. One of the main open problems left by their work is whether the accuracy, i.e., the approximation ratio and additive erro…

This https://arxiv.org/abs/2311.09393 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPL_…

Taypsi: Static Enforcement of Privacy Policies for Policy-Agnostic Oblivious Computation
Secure multiparty computation (MPC) techniques enable multiple parties to compute joint functions over their private data without sharing that data to other parties, typically by employing powerful cryptographic protocols to protect individual's data. One challenge when writing such functions is that most MPC languages force users to intermix programmatic and privacy concerns in a single application, making it difficult to change or audit a program's underlying privacy policy. Existing policy-a…

This https://arxiv.org/abs/2404.01625 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

AAA: an Adaptive Mechanism for Locally Differential Private Mean Estimation
Local differential privacy (LDP) is a strong privacy standard that has been adopted by popular software systems. The main idea is that each individual perturbs their own data locally, and only submits the resulting noisy version to a data aggregator. Although much effort has been devoted to computing various types of aggregates and building machine learning applications under LDP, research on fundamental perturbation mechanisms has not achieved significant improvement in recent years. Towards a…

Privacy-Preserving Autoencoder for Collaborative Object Detection
Bardia Azizian, Ivan Bajic
https://arxiv.org/abs/2402.18864 https://

Privacy-Preserving Autoencoder for Collaborative Object Detection
Privacy is a crucial concern in collaborative machine vision where a part of a Deep Neural Network (DNN) model runs on the edge, and the rest is executed on the cloud. In such applications, the machine vision model does not need the exact visual content to perform its task. Taking advantage of this potential, private information could be removed from the data insofar as it does not significantly impair the accuracy of the machine vision system. In this paper, we present an autoencoder-style net…

This https://arxiv.org/abs/2312.15591 has been replaced.
link: https://scholar.google.com/scholar?q=a

Privacy-Preserved Neural Graph Databases
In the era of large language models (LLMs), efficient and accurate data retrieval has become increasingly crucial for the use of domain-specific or private data in the retrieval augmented generation (RAG). Neural graph databases (NGDBs) have emerged as a powerful paradigm that combines the strengths of graph databases (GDBs) and neural networks to enable efficient storage, retrieval, and analysis of graph-structured data which can be adaptively trained with LLMs. The usage of neural embedding s…

Differentially Private Bayesian Persuasion
Yuqi Pan, Zhiwei Steven Wu, Haifeng Xu, Shuran Zheng
https://arxiv.org/abs/2402.15872 https://

Differentially Private Bayesian Persuasion
The tension between persuasion and privacy preservation is common in real-world settings. Online platforms should protect the privacy of web users whose data they collect, even as they seek to disclose information about these data to selling advertising spaces. Similarly, hospitals may share patient data to attract research investments with the obligation to preserve patients' privacy. To deal with these issues, we develop a framework to study Bayesian persuasion under differential privacy cons…

This https://arxiv.org/abs/2402.04273 has been replaced.
link: https://scholar.google.com/scholar?q=a

Breaking Data Silos: Cross-Domain Learning for Multi-Agent Perception from Independent Private Sources
The diverse agents in multi-agent perception systems may be from different companies. Each company might use the identical classic neural network architecture based encoder for feature extraction. However, the data source to train the various agents is independent and private in each company, leading to the Distribution Gap of different private data for training distinct agents in multi-agent perception system. The data silos by the above Distribution Gap could result in a significant performan…

Publish your data! Not PDFs. A clear ask for #OpendataCH from a team at the #OpenFarmingHackdays2024 who used a combination of public and private sources for their predictive farming #datascience

Schädlingsprognose mit Daten neu interpretiert
A small ML project that attempts to predict the occurrence of pests based on historical data.

This https://arxiv.org/abs/2305.12650 has been replaced.
link: https://scholar.google.com/scholar?q=a

When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions
Federated recommendation system usually trains a global model on the server without direct access to users' private data on their own devices. However, this separation of the recommendation model and users' private data poses a challenge in providing quality service, particularly when it comes to new items, namely cold-start recommendations in federated settings. This paper introduces a novel method called Item-aligned Federated Aggregation (IFedRec) to address this challenge. It is the first r…

Advancing a Consent-Forward Paradigm for Digital Mental Health Data
Sachin R. Pendse, Logan Stapleton, Neha Kumar, Munmun De Choudhury, Stevie Chancellor
https://arxiv.org/abs/2404.14548

Advancing a Consent-Forward Paradigm for Digital Mental Health Data
The field of digital mental health is advancing at a rapid pace. Passively collected data from user engagements with digital tools and services continue to contribute new insights into mental health and illness. As the field of digital mental health grows, a concerning norm has been established -- digital service users are given little say over how their data is collected, shared, or used to generate revenue for private companies. Given a long history of service user exclusion from data collect…

This https://arxiv.org/abs/2312.14388 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility
The shuffle model of local differential privacy is an advanced method of privacy amplification designed to enhance privacy protection with high utility. It achieves this by randomly shuffling sensitive data, making linking individual data points to specific individuals more challenging. However, most existing studies have focused on the shuffle model based on $(ε_0,0)$-Locally Differentially Private (LDP) randomizers, with limited consideration for complex scenarios such as $(ε_0,δ_0)$-LDP o…

This https://arxiv.org/abs/2305.10416 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Minimax rate for multivariate data under componentwise local differential privacy constraints
Our research delves into the balance between maintaining privacy and preserving statistical accuracy when dealing with multivariate data that is subject to \textit{componentwise local differential privacy} (CLDP). With CLDP, each component of the private data is made public through a separate privacy channel. This allows for varying levels of privacy protection for different components or for the privatization of each component by different entities, each with their own distinct privacy policie…

96% of US Hospital Websites Share Visitor Info With Meta, Google, Data Brokers - Slashdot
https://yro.slashdot.org/story/24/04/12/2046230/96-of-us-hospital-websites-share-visitor-info-with-meta-google-data-…

96% of US Hospital Websites Share Visitor Info With Meta, Google, Data Brokers - Slashdot
An anonymous reader quotes a report from The Guardian: Hospitals -- despite being places where people implicitly expect to have their personal details kept private -- frequently use tracking technologies on their websites to share user information with Google, Meta, data brokers, and other third par...

🏈 @… #CFB #CollegeFootball
Via Wes Blankenship @Wes_nship
·
4h
Taylor Swift isn't built for the pressures of big-time College Football
Via…

Are you a student? Wanna work with me as a data scientist intern? https://telemetrydeck.com/jobs/#h52e68259f2da

Jobs | TelemetryDeck
🧡 Helping developers improve their apps with immediate, accurate and private analytics.

This is probably too hacky but can't help trying it out. Phanpy's shortcuts settings are saved as (private) notes on *my own* profile, which will allow export/import in the "cloud".
It's possible to use the API to add notes for *other* profiles, and they're hidden from (self, currently-logged-in) own profile. I'm using it to store data 🤪
NOT available on the dev site yet, still on my local machine.

Federated Transfer Component Analysis Towards Effective VNF Profiling
Xunzheng ZhangB, Shadi Moazzeni, Juan Marcelo Parra-Ullauri, Reza Nejabati, Dimitra Simeonidou
https://arxiv.org/abs/2404.17553

Federated Transfer Component Analysis Towards Effective VNF Profiling
The increasing concerns of knowledge transfer and data privacy challenge the traditional gather-and-analyse paradigm in networks. Specifically, the intelligent orchestration of Virtual Network Functions (VNFs) requires understanding and profiling the resource consumption. However, profiling all kinds of VNFs is time-consuming. It is important to consider transferring the well-profiled VNF knowledge to other lack-profiled VNF types while keeping data private. To this end, this paper proposes a F…

This https://arxiv.org/abs/2312.14388 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility
The shuffle model of local differential privacy is an advanced method of privacy amplification designed to enhance privacy protection with high utility. It achieves this by randomly shuffling sensitive data, making linking individual data points to specific individuals more challenging. However, most existing studies have focused on the shuffle model based on $(ε_0,0)$-Locally Differentially Private (LDP) randomizers, with limited consideration for complex scenarios such as $(ε_0,δ_0)$-LDP o…

Würk, a Colorado-based HR platform for providing payrolls, managing the workforce, and compliance for the cannabis industry, exposed the private data of more than 2.5 million users.
https://cybernews.com/security/cannabis-dispensary-wurk-data-leak/

Tell Congress: No TikTok ban. Ban illicit data collection generally by anyone, instead:
#EFF

Tell Congress: Stop the TikTok Ban
Congress is fast-tracking a bill that would effectively ban TikTok in the US, but do little for its alleged goal of protecting our private information and the collection of our data by foreign governments. Tell Congress: Instead of giving the President the power to ban entire social media platforms based on their country of origin, our representatives should focus on what matters—protecting our data no matter who is collecting it.

This https://arxiv.org/abs/2403.02324 has been replaced.
link: https://scholar.google.com/scholar?q=a

Differentially Private Communication of Measurement Anomalies in the Smart Grid
In this paper, we present a framework based on differential privacy (DP) for querying electric power measurements to detect system anomalies or bad data. Our DP approach conceals consumption and system matrix data, while simultaneously enabling an untrusted third party to test hypotheses of anomalies, such as the presence of bad data, by releasing a randomized sufficient statistic for hypothesis-testing. We consider a measurement model corrupted by Gaussian noise and a sparse noise vector repre…

This https://arxiv.org/abs/2312.15591 has been replaced.
link: https://scholar.google.com/scholar?q=a

Privacy-Preserved Neural Graph Databases
In the era of large language models (LLMs), efficient and accurate data retrieval has become increasingly crucial for the use of domain-specific or private data in the retrieval augmented generation (RAG). Neural graph databases (NGDBs) have emerged as a powerful paradigm that combines the strengths of graph databases (GDBs) and neural networks to enable efficient storage, retrieval, and analysis of graph-structured data which can be adaptively trained with LLMs. The usage of neural embedding s…

#Microsoft and #OpenAI are drawing up plans for a $100 billion #AI #supercomputer
The supercomputer, codenamed "

Microsoft and OpenAI Plot $100 Billion Stargate AI Supercomputer
Executives at Microsoft and OpenAI have been drawing up plans for a data center project that would contain a supercomputer with millions of specialized server chips to power OpenAI’s artificial intelligence, according to three people who have been involved in the private conversations about the ...

The #textpocalypse is already killing people and they're not even dead. Obituary spam takes advantage of what Google calls "data voids" to send AI-generated obits by AI-generated journos to the top of search rankings. And the subjects of these eulogies aren't always dead.

How much of this is TSwift vs. her lawyers just looking for someone to bully? Is she even aware of this Threads account?
It's public info. Don't want to have your flight data and information exposed to the world? Don't fly private. It's better for the environment that way too! https://www…

Taylor Swift has bad blood with student tracking celebrity flights via public info
Taylor Swift’s attorney sent a cease-and-desist letter threatening legal action over a college student’s jet-tracking social media accounts.

A look at Android 15's Private Space feature, similar to Samsung's Secure Folder, which creates a separate profile for hiding apps and data (Mishaal Rahman/Android Authority)
https://www.androidauthority.com/android-15-private-space-hands-on-343…

Here is Android 15's new Private Space in action
Android 15 will introduce a new Private Space feature that creates a separate profile you can hide apps in.

Differentially Private Release of Israel's National Registry of Live Births
Shlomi Hod, Ran Canetti
https://arxiv.org/abs/2405.00267 https://

Differentially Private Release of Israel's National Registry of Live Births
In February 2024, Israel's Ministry of Health released microdata of live births in Israel in 2014. The dataset is based on Israel's National Registry of Live Births and offers substantial value in multiple areas, such as scientific research and policy-making. At the same time, the data was processed so as to protect the privacy of 2014's mothers and newborns. The release was co-designed by the authors together with stakeholders from both inside and outside the Ministry of Health. This paper pre…

This https://arxiv.org/abs/2302.01622 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
Artificial intelligence (AI) models are increasingly used in the medical domain. However, as medical data is highly sensitive, special precautions to ensure its protection are required. The gold standard for privacy preservation is the introduction of differential privacy (DP) to model training. Prior work indicates that DP has negative implications on model accuracy and fairness, which are unacceptable in medicine and represent a main barrier to the widespread use of privacy-preserving techniq…

Differentially Private Federated Learning: Servers Trustworthiness, Estimation, and Statistical Inference
Zhe Zhang, Ryumei Nakada, Linjun Zhang
https://arxiv.org/abs/2404.16287 <…

Differentially Private Federated Learning: Servers Trustworthiness, Estimation, and Statistical Inference
Differentially private federated learning is crucial for maintaining privacy in distributed environments. This paper investigates the challenges of high-dimensional estimation and inference under the constraints of differential privacy. First, we study scenarios involving an untrusted central server, demonstrating the inherent difficulties of accurate estimation in high-dimensional problems. Our findings indicate that the tight minimax rates depends on the high-dimensionality of the data even w…

I’m always enamored with side-channel attacks. Pulling data from unexpected sources is fascinating and difficult to predict.
This isn’t particularly novel — padding data to hide length is part of the toolbox — but new systems making old mistakes is also fun!
https://<…

Hackers can read private AI-assistant chats even though they’re encrypted
All non-Google chat GPTs affected by side channel that leaks responses sent to users.

Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data
Dayananda Herurkar, Sebastian Palacio, Ahmed Anwar, Joern Hees, Andreas Dengel
https://arxiv.org/abs/2404.14933 <…

Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data
Anomaly detection in real-world scenarios poses challenges due to dynamic and often unknown anomaly distributions, requiring robust methods that operate under an open-world assumption. This challenge is exacerbated in practical settings, where models are employed by private organizations, precluding data sharing due to privacy and competitive concerns. Despite potential benefits, the sharing of anomaly information across organizations is restricted. This paper addresses the question of enhancin…

Oauth implementation flaws allow access to private repos via ChatGPT plugins https://salt.security/blog/security-flaws-within-chatgpt-extensions-allowed-access-to-accounts-on-third-party-websites-and-sensitive-data?utm_source=social&utm_medium=reddit

Enforce privacy laws! Enforce monopoly laws! Enforce NEW security laws!
The giant corporation "responsible" for handling our "private" data is UnitedHealth Group Incorporated. Their market capitalization is almost $.5 trillion. They can be forced by law, to spend $billions on new computer information security measures. Then they can afford to be broken into smaller, more responsible, corporations.

96% of US Hospital Websites Share Visitor Info With Meta, Google, Data Brokers - Slashdot
https://yro.slashdot.org/story/24/04/12/2046230/96-of-us-hospital-websites-share-visitor-info-with-meta-google-data-…

96% of US Hospital Websites Share Visitor Info With Meta, Google, Data Brokers - Slashdot
An anonymous reader quotes a report from The Guardian: Hospitals -- despite being places where people implicitly expect to have their personal details kept private -- frequently use tracking technologies on their websites to share user information with Google, Meta, data brokers, and other third par...

Towards actionability for open medical imaging datasets: lessons from community-contributed platforms for data management and stewardship
Amelia Jim\'enez-S\'anchez, Natalia-Rozalia Avlona, Dovile Juodelyte, Th\'eo Sourget, Caroline Vang-Larsen, Hubert Dariusz Zaj\k{a}c, Veronika Cheplygina
https://arxiv.org/abs/2402…

Towards actionability for open medical imaging datasets: lessons from community-contributed platforms for data management and stewardship
Medical imaging datasets are fundamental to artificial intelligence (AI) in healthcare. The accuracy, robustness and fairness of diagnostic algorithms depend on the data (and its quality) on which the models are trained and evaluated. Medical imaging datasets have become increasingly available to the public, and are often hosted on Community-Contributed Platforms (CCP), including private companies like Kaggle or HuggingFace. While open data is important to enhance the redistribution of data's p…

This is probably too hacky but can't help trying it out. Phanpy's shortcuts settings are saved as (private) notes on *my own* profile, which will allow export/import in the "cloud".
It's possible to use the API to add notes for *other* profiles, and they're hidden from (self, currently-logged-in) own profile. I'm using it to store data 🤪
NOT available on the dev site yet, still on my local machine.

Six homeless people die every day on the streets of Los Angeles. Without cops enforcing private property rights we could solve this problem practically tomorrow. These people are killed by police just as surely as the people they gun down. This is in addition to the millions whose lives are shortened and immiserated by being forced to sell their labor to survive. These people are also killed by police.

SOLARNET Metadata Recommendations for Simulated Data
Stein Vidar Haugan, Terje Fredvik
https://arxiv.org/abs/2403.12241 https://arxiv…

SOLARNET Metadata Recommendations for Simulated Data
Until the advent of the SOLARNET recommendations, metadata sharing of simulated data within the Solar Physics community has been mostly on a "private communication" basis, with the description of the data format and content conveyed in an ad hoc manner. This document aims to amend this situation by establishing recommendations for representing such data and the associated metadata, based on the SOLARNET Metadata Recommendations for Solar Observations (arXiv:2011.12139)

Celebrating 15 Years of Surveillance Self-Defense from the Electronic Frontier Foundation: https://www.eff.org/deeplinks/2024/03/celebrating-15-years-surveillance-self-defense

Ever thought about monitoring your sleep rhythm with a #smartwatch? Concerned, that your private data would be sent to the manufacturer's cloud? The #privacy friendly solution is Gadgetbridge, an Android App, that implements all important smartwatch functions, but without sending data to some cloud…

#Microsoft and #OpenAI are drawing up plans for a $100 billion #AI #supercomputer
The supercomputer, codenamed "

Microsoft and OpenAI Plot $100 Billion Stargate AI Supercomputer
Executives at Microsoft and OpenAI have been drawing up plans for a data center project that would contain a supercomputer with millions of specialized server chips to power OpenAI’s artificial intelligence, according to three people who have been involved in the private conversations about the ...

This https://arxiv.org/abs/2402.04765 has been replaced.
link: https://scholar.google.com/scholar?q=a

Measuring the performance of investments in information security startups: An empirical analysis by cybersecurity sectors using Crunchbase data
Early-stage firms play a significant role in driving innovation and creating new products and services, especially for cybersecurity. Therefore, evaluating their performance is crucial for investors and policymakers. This work presents a financial evaluation of early-stage firms' performance in 19 cybersecurity sectors using a private-equity dataset from 2010 to 2022 retrieved from Crunchbase. We observe firms, their primary and secondary activities, funding rounds, and pre and post-money valua…

Sources: FISA's Section 702 bill was shelved over a rule that would end the government's ability to pay US firms for information rather than serving a warrant (Dell Cameron/Wired)
https://www.wired.com/story/section-702-privacy-reforms-sabotage-c…

Leak of Russian Threat Part of a Bid to Kill US Surveillance Reform, Sources Say
A surprise disclosure of a national security threat by the House Intelligence chair was part of an effort to block legislation that aimed to limit cops and spies from buying Americans' private data.

Two-step Estimation of Network Formation Models with Unobserved Heterogeneities and Strategic Interactions
Shaomin Wu
https://arxiv.org/abs/2404.12581 http…

Two-step Estimation of Network Formation Models with Unobserved Heterogeneities and Strategic Interactions
In this paper, I characterize the network formation process as a static game of incomplete information, where the latent payoff of forming a link between two individuals depends on the structure of the network, as well as private information on agents' attributes. I allow agents' private unobserved attributes to be correlated with observed attributes through individual fixed effects. Using data from a single large network, I propose a two-step estimator for the model primitives. In the first st…

Bistochastically private release of data streams with zero delay
Nicolas Ruiz
https://arxiv.org/abs/2402.16094 https://arxiv.org/pdf/…

Bistochastically private release of data streams with zero delay
Although the bulk of the research in privacy and statistical disclosure control is designed for static data, more and more data are often collected as continuous streams, and extensions of popular privacy tools and models have been proposed for this scenario. However, most of these proposals require buffers, where incoming individuals are momentarily stored, anonymized, and then released following a delay, thus considering a data stream as a succession of batches while it is by nature continuou…

Oauth implementation flaws allow access to private repos via ChatGPT plugins https://salt.security/blog/security-flaws-within-chatgpt-extensions-allowed-access-to-accounts-on-third-party-websites-and-sensitive-data?utm_source=social&utm_medium=reddit

This https://arxiv.org/abs/2210.06459 has been replaced.
link: https://scholar.google.com/scholar?q=a

Differentially private multivariate medians
Statistical tools which satisfy rigorous privacy guarantees are necessary for modern data analysis. It is well-known that robustness against contamination is linked to differential privacy. Despite this fact, using multivariate medians for differentially private and robust multivariate location estimation has not been systematically studied. We develop novel finite-sample performance guarantees for differentially private multivariate depth-based medians, which are essentially sharp. Our results…

Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection
Ziyu Wang, Zhongqi Yang, Iman Azimi, Amir M. Rahmani
https://arxiv.org/abs/2402.10862

Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection
Mental health conditions, prevalent across various demographics, necessitate efficient monitoring to mitigate their adverse impacts on life quality. The surge in data-driven methodologies for mental health monitoring has underscored the importance of privacy-preserving techniques in handling sensitive health data. Despite strides in federated learning for mental health monitoring, existing approaches struggle with vulnerabilities to certain cyber-attacks and data insufficiency in real-world app…

Is open source software culture enough to make AI a common ?
Robin Quillivic, Salma Mesmoudi
https://arxiv.org/abs/2403.12774 https://

Is open source software culture enough to make AI a common ?
Language models (LM or LLM) are increasingly deployed in the field of artificial intelligence (AI) and its applications, but the question arises as to whether they can be a common resource managed and maintained by a community of users. Indeed, the dominance of private companies with exclusive access to massive data and language processing resources can create inequalities and biases in LM, as well as obstacles to innovation for those who do not have the same resources necessary for their imple…

Sources: FISA's Section 702 bill was shelved over a rule that would end the government's ability to pay US firms for information rather than serving a warrant (Dell Cameron/Wired)
https://www.wired.com/story/section-702-privacy-reforms-sabotage-c…

Leak of Russian Threat Part of a Bid to Kill US Surveillance Reform, Sources Say
A surprise disclosure of a national security threat by the House Intelligence chair was part of an effort to block legislation that aimed to limit cops and spies from buying Americans' private data.

This https://arxiv.org/abs/2311.09393 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPL_…

Taypsi: Static Enforcement of Privacy Policies for Policy-Agnostic Oblivious Computation
Secure multiparty computation (MPC) techniques enable multiple parties to compute joint functions over their private data without sharing that data to other parties, typically by employing powerful cryptographic protocols to protect individual's data. One challenge when writing such functions is that most MPC languages force users to intermix programmatic and privacy concerns in a single application, making it difficult to change or audit a program's underlying privacy policy. Existing policy-a…

BC-MRI-SEG: A Breast Cancer MRI Tumor Segmentation Benchmark
Anthony Bilic, Chen Chen
https://arxiv.org/abs/2404.13756 https://arxiv.…

BC-MRI-SEG: A Breast Cancer MRI Tumor Segmentation Benchmark
Binary breast cancer tumor segmentation with Magnetic Resonance Imaging (MRI) data is typically trained and evaluated on private medical data, which makes comparing deep learning approaches difficult. We propose a benchmark (BC-MRI-SEG) for binary breast cancer tumor segmentation based on publicly available MRI datasets. The benchmark consists of four datasets in total, where two datasets are used for supervised training and evaluation, and two are used for zero-shot evaluation. Additionally we…

Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning
Joshua C. Zhao, Ahaan Dabholkar, Atul Sharma, Saurabh Bagchi
https://arxiv.org/abs/2403.18144

Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning
Federated learning is a decentralized learning paradigm introduced to preserve privacy of client data. Despite this, prior work has shown that an attacker at the server can still reconstruct the private training data using only the client updates. These attacks are known as data reconstruction attacks and fall into two major categories: gradient inversion (GI) and linear layer leakage attacks (LLL). However, despite demonstrating the effectiveness of these attacks in breaching privacy, prior wo…

Two-step Estimation of Network Formation Models with Unobserved Heterogeneities and Strategic Interactions
Shaomin Wu
https://arxiv.org/abs/2404.12581 http…

Two-step Estimation of Network Formation Models with Unobserved Heterogeneities and Strategic Interactions
In this paper, I characterize the network formation process as a static game of incomplete information, where the latent payoff of forming a link between two individuals depends on the structure of the network, as well as private information on agents' attributes. I allow agents' private unobserved attributes to be correlated with observed attributes through individual fixed effects. Using data from a single large network, I propose a two-step estimator for the model primitives. In the first st…

pfl-research: simulation framework for accelerating research in Private Federated Learning
Filip Granqvist, Congzheng Song, \'Aine Cahill, Rogier van Dalen, Martin Pelikan, Yi Sheng Chan, Xiaojun Feng, Natarajan Krishnaswami, Vojta Jina, Mona Chitnis
https://arxiv.org/abs/2404.06430 …

pfl-research: simulation framework for accelerating research in Private Federated Learning
Federated learning (FL) is an emerging machine learning (ML) training paradigm where clients own their data and collaborate to train a global model, without revealing any data to the server and other participants. Researchers commonly perform experiments in a simulation environment to quickly iterate on ideas. However, existing open-source tools do not offer the efficiency required to simulate FL on larger and more realistic FL datasets. We introduce pfl-research, a fast, modular, and easy-to-u…

Real-Time Trajectory Synthesis with Local Differential Privacy
Yujia Hu, Yuntao Du, Zhikun Zhang, Ziquan Fang, Lu Chen, Kai Zheng, Yunjun Gao
https://arxiv.org/abs/2404.11450

Real-Time Trajectory Synthesis with Local Differential Privacy
Trajectory streams are being generated from location-aware devices, such as smartphones and in-vehicle navigation systems. Due to the sensitive nature of the location data, directly sharing user trajectories suffers from privacy leakage issues. Local differential privacy (LDP), which perturbs sensitive data on the user side before it is shared or analyzed, emerges as a promising solution for private trajectory stream collection and analysis. Unfortunately, existing stream release approaches oft…

This https://arxiv.org/abs/2312.14388 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility
The shuffle model of local differential privacy is an advanced method of privacy amplification designed to enhance privacy protection with high utility. It achieves this by randomly shuffling sensitive data, making linking individual data points to specific individuals more challenging. However, most existing studies have focused on the shuffle model based on $(ε_0,0)$-Locally Differentially Private (LDP) randomizers, with limited consideration for complex scenarios such as $(ε_0,δ_0)$-LDP o…

This https://arxiv.org/abs/2311.09393 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPL_…

Taypsi: Static Enforcement of Privacy Policies for Policy-Agnostic Oblivious Computation
Secure multiparty computation (MPC) techniques enable multiple parties to compute joint functions over their private data without sharing that data with other parties, typically by employing powerful cryptographic protocols to protect individual's data. One challenge when writing such functions is that most MPC languages force users to intermix programmatic and privacy concerns in a single application, making it difficult to change or audit a program's underlying privacy policy. Prior policy-ag…

CPR: Retrieval Augmented Generation for Copyright Protection
Aditya Golatkar, Alessandro Achille, Luca Zancato, Yu-Xiang Wang, Ashwin Swaminathan, Stefano Soatto
https://arxiv.org/abs/2403.18920

CPR: Retrieval Augmented Generation for Copyright Protection
Retrieval Augmented Generation (RAG) is emerging as a flexible and robust technique to adapt models to private users data without training, to handle credit attribution, and to allow efficient machine unlearning at scale. However, RAG techniques for image generation may lead to parts of the retrieved samples being copied in the model's output. To reduce risks of leaking private information contained in the retrieved set, we introduce Copy-Protected generation with Retrieval (CPR), a new method …

Online Differentially Private Synthetic Data Generation
Yiyun He, Roman Vershynin, Yizhe Zhu
https://arxiv.org/abs/2402.08012 https://

Online Differentially Private Synthetic Data Generation
We present a polynomial-time algorithm for online differentially private synthetic data generation. For a data stream within the hypercube $[0,1]^d$ and an infinite time horizon, we develop an online algorithm that generates a differentially private synthetic dataset at each time $t$. This algorithm achieves a near-optimal accuracy bound of $O(t^{-1/d}\log(t))$ for $d\geq 2$ and $O(t^{-1}\log^{4.5}(t))$ for $d=1$ in the 1-Wasserstein distance. This result generalizes the previous work on the co…

This https://arxiv.org/abs/2302.01622 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
Artificial intelligence (AI) models are increasingly used in the medical domain. However, as medical data is highly sensitive, special precautions to ensure its protection are required. The gold standard for privacy preservation is the introduction of differential privacy (DP) to model training. Prior work indicates that DP has negative implications on model accuracy and fairness, which are unacceptable in medicine and represent a main barrier to the widespread use of privacy-preserving techniq…

TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data
Richeng Jin, Yujie Gu, Kai Yue, Xiaofan He, Zhaoyang Zhang, Huaiyu Dai
https://arxiv.org/abs/2402.10816

This https://arxiv.org/abs/2312.05264 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

All Rivers Run to the Sea: Private Learning with Asymmetric Flows
Data privacy is of great concern in cloud machine-learning service platforms, when sensitive data are exposed to service providers. While private computing environments (e.g., secure enclaves), and cryptographic approaches (e.g., homomorphic encryption) provide strong privacy protection, their computing performance still falls short compared to cloud GPUs. To achieve privacy protection with high computing performance, we propose Delta, a new private training and inference framework, with compar…

CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources
Sikha Pentyala, Mayana Pereira, Martine De Cock
https://arxiv.org/abs/2402.08614

CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources
Data is the lifeblood of the modern world, forming a fundamental part of AI, decision-making, and research advances. With increase in interest in data, governments have taken important steps towards a regulated data world, drastically impacting data sharing and data usability and resulting in massive amounts of data confined within the walls of organizations. While synthetic data generation (SDG) is an appealing solution to break down these walls and enable data sharing, the main drawback of ex…

Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models
Zihan Fang, Zheng Lin, Zhe Chen, Xianhao Chen, Yue Gao, Yuguang Fang
https://arxiv.org/abs/2404.06448

Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models
Recently, there has been a surge in the development of advanced intelligent generative content (AIGC), especially large language models (LLMs). However, for many downstream tasks, it is necessary to fine-tune LLMs using private data. While federated learning offers a promising privacy-preserving solution to LLM fine-tuning, the substantial size of an LLM, combined with high computational and communication demands, makes it hard to apply to downstream tasks. More importantly, private edge server…

Efficiently Computing Similarities to Private Datasets
Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski
https://arxiv.org/abs/2403.08917

Efficiently Computing Similarities to Private Datasets
Many methods in differentially private model training rely on computing the similarity between a query point (such as public or synthetic data) and private data. We abstract out this common subroutine and study the following fundamental algorithmic problem: Given a similarity function $f$ and a large high-dimensional private dataset $X \subset \mathbb{R}^d$, output a differentially private (DP) data structure which approximates $\sum_{x \in X} f(x,y)$ for any query $y$. We consider the cases wh…

This https://arxiv.org/abs/2204.09167 has been replaced.
link: https://scholar.google.com/scholar?q=a

Private measures, random walks, and synthetic data
Differential privacy is a mathematical concept that provides an information-theoretic security guarantee. While differential privacy has emerged as a de facto standard for guaranteeing privacy in data sharing, the known mechanisms to achieve it come with some serious limitations. Utility guarantees are usually provided only for a fixed, a priori specified set of queries. Moreover, there are no utility guarantees for more complex - but very common - machine learning tasks such as clustering or c…

Private Count Release: A Simple and Scalable Approach for Private Data Analytics
Ryan Rogers
https://arxiv.org/abs/2403.05073 https://

Private Count Release: A Simple and Scalable Approach for Private Data Analytics
We present a data analytics system that ensures accurate counts can be released with differential privacy and minimal onboarding effort while showing instances that outperform other approaches that require more onboarding effort. The primary difference between our proposal and existing approaches is that it does not rely on user contribution bounds over distinct elements, i.e. $\ell_0$-sensitivity bounds, which can significantly bias counts. Contribution bounds for $\ell_0$-sensitivity have bee…

Private Count Release: A Simple and Scalable Approach for Private Data Analytics
Ryan Rogers
https://arxiv.org/abs/2403.05073 https://

Private Count Release: A Simple and Scalable Approach for Private Data Analytics
We present a data analytics system that ensures accurate counts can be released with differential privacy and minimal onboarding effort while showing instances that outperform other approaches that require more onboarding effort. The primary difference between our proposal and existing approaches is that it does not rely on user contribution bounds over distinct elements, i.e. $\ell_0$-sensitivity bounds, which can significantly bias counts. Contribution bounds for $\ell_0$-sensitivity have bee…

This https://arxiv.org/abs/2309.08248 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Verifiable Privacy-Preserving Computing
Privacy-preserving computation (PPC) methods, such as secure multiparty computation (MPC) and homomorphic encryption (HE), are deployed increasingly often to guarantee data confidentiality in computations over private, distributed data. Similarly, we observe a steep increase in the adoption of zero-knowledge proofs (ZKPs) to guarantee (public) verifiability of locally executed computations. We project that applications that are data intensive and require strong privacy guarantees, are also like…

Privacy-Preserving Instructions for Aligning Large Language Models
Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu
https://arxiv.org/abs/2402.13659 https://

Privacy-Preserving Instructions for Aligning Large Language Models
Service providers of large language model (LLM) applications collect user instructions in the wild and use them in further aligning LLMs with users' intentions. These instructions, which potentially contain sensitive information, are annotated by human workers in the process. This poses a new privacy risk not addressed by the typical private optimization. To this end, we propose using synthetic instructions to replace real instructions in data annotation and model fine-tuning. Formal differenti…

SoK: Can Trajectory Generation Combine Privacy and Utility?
Erik Buchholz, Alsharif Abuadbba, Shuo Wang, Surya Nepal, Salil S. Kanhere
https://arxiv.org/abs/2403.07218

SoK: Can Trajectory Generation Combine Privacy and Utility?
While location trajectories represent a valuable data source for analyses and location-based services, they can reveal sensitive information, such as political and religious preferences. Differentially private publication mechanisms have been proposed to allow for analyses under rigorous privacy guarantees. However, the traditional protection schemes suffer from a limiting privacy-utility trade-off and are vulnerable to correlation and reconstruction attacks. Synthetic trajectory data generatio…

This https://arxiv.org/abs/2404.03216 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Accurate Low-Degree Polynomial Approximation of Non-polynomial Operators for Fast Private Inference in Homomorphic Encryption
As machine learning (ML) permeates fields like healthcare, facial recognition, and blockchain, the need to protect sensitive data intensifies. Fully Homomorphic Encryption (FHE) allows inference on encrypted data, preserving the privacy of both data and the ML model. However, it slows down non-secure inference by up to five magnitudes, with a root cause of replacing non-polynomial operators (ReLU and MaxPooling) with high-degree Polynomial Approximated Function (PAF). We propose SmartPAF, a fra…

Advances in Differential Privacy and Differentially Private Machine Learning
Saswat Das, Subhankar Mishra
https://arxiv.org/abs/2404.04706 https://<…

Advances in Differential Privacy and Differentially Private Machine Learning
There has been an explosion of research on differential privacy (DP) and its various applications in recent years, ranging from novel variants and accounting techniques in differential privacy to the thriving field of differentially private machine learning (DPML) to newer implementations in practice, like those by various companies and organisations such as census bureaus. Most recent surveys focus on the applications of differential privacy in particular contexts like data publishing, specifi…

This https://arxiv.org/abs/2306.08745 has been replaced.
link: https://scholar.google.com/scholar?q=a

PLAN: Variance-Aware Private Mean Estimation
Differentially private mean estimation is an important building block in privacy-preserving algorithms for data analysis and machine learning. Though the trade-off between privacy and utility is well understood in the worst case, many datasets exhibit structure that could potentially be exploited to yield better algorithms. In this paper we present $\textit{Private Limit Adapted Noise}$ (PLAN), a family of differentially private algorithms for mean estimation in the setting where inputs are ind…

This https://arxiv.org/abs/2306.03013 has been replaced.
link: https://scholar.google.com/scholar?q=a

Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning
Malicious server (MS) attacks have enabled the scaling of data stealing in federated learning to large batch sizes and secure aggregation, settings previously considered private. However, many concerns regarding the client-side detectability of MS attacks were raised, questioning their practicality. In this work, for the first time, we thoroughly study client-side detectability. We first demonstrate that all prior MS attacks are detectable by principled checks, and formulate a necessary set of …

This https://arxiv.org/abs/2207.06944 has been replaced.
link: https://scholar.google.com/scholar?q=a

Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank
Personalized PageRank (PPR) is a fundamental tool in unsupervised learning of graph representations such as node ranking, labeling, and graph embedding. However, while data privacy is one of the most important recent concerns, existing PPR algorithms are not designed to protect user privacy. PPR is highly sensitive to the input graph edges: the difference of only one edge may cause a big change in the PPR vector, potentially leaking private user data. In this work, we propose an algorithm whi…

This https://arxiv.org/abs/2306.08538 has been replaced.
link: https://scholar.google.com/scholar?q=a

Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions
Machine Learning as a Service (MLaaS) is an increasingly popular design where a company with abundant computing resources trains a deep neural network and offers query access for tasks like image classification. The challenge with this design is that MLaaS requires the client to reveal their potentially sensitive queries to the company hosting the model. Multi-party computation (MPC) protects the client's data by allowing encrypted inferences. However, current approaches suffer from prohibitive…

This https://arxiv.org/abs/2403.13041 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Provable Privacy with Non-Private Pre-Processing
When analysing Differentially Private (DP) machine learning pipelines, the potential privacy cost of data-dependent pre-processing is frequently overlooked in privacy accounting. In this work, we propose a general framework to evaluate the additional privacy cost incurred by non-private data-dependent pre-processing algorithms. Our framework establishes upper bounds on the overall privacy guarantees by utilising two new technical notions: a variant of DP termed Smooth DP and the bounded sensiti…

A Method for Decrypting Data Infected with Rhysida Ransomware
Giyoon Kim, Soojin Kang, Seungjun Baek, Kimoon Kim, Jongsung Kim
https://arxiv.org/abs/2402.06440

A Method for Decrypting Data Infected with Rhysida Ransomware
Ransomware is malicious software that is a prominent global cybersecurity threat. Typically, ransomware encrypts data on a system, rendering the victim unable to decrypt it without the attacker's private key. Subsequently, victims often pay a substantial ransom to recover their data, yet some may still incur damage or loss. This study examines Rhysida ransomware, which caused significant damage in the second half of 2023, and proposes a decryption method. Rhysida ransomware employed a secure ra…

Accurate Low-Degree Polynomial Approximation of Non-polynomial Operators for Fast Private Inference in Homomorphic Encryption
Jianming Tong, Jingtian Dang, Anupam Golder, Callie Hao, Arijit Raychowdhury, Tushar Krishna
https://arxiv.org/abs/2404.03216

Accurate Low-Degree Polynomial Approximation of Non-polynomial Operators for Fast Private Inference in Homomorphic Encryption
As machine learning (ML) permeates fields like healthcare, facial recognition, and blockchain, the need to protect sensitive data intensifies. Fully Homomorphic Encryption (FHE) allows inference on encrypted data, preserving the privacy of both data and the ML model. However, it slows down non-secure inference by up to five magnitudes, with a root cause of replacing non-polynomial operators (ReLU and MaxPooling) with high-degree Polynomial Approximated Function (PAF). We propose SmartPAF, a fra…

This https://arxiv.org/abs/2403.13041 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Provable Privacy with Non-Private Pre-Processing
When analysing Differentially Private (DP) machine learning pipelines, the potential privacy cost of data-dependent pre-processing is frequently overlooked in privacy accounting. In this work, we propose a general framework to evaluate the additional privacy cost incurred by non-private data-dependent pre-processing algorithms. Our framework establishes upper bounds on the overall privacy guarantees by utilising two new technical notions: a variant of DP termed Smooth DP and the bounded sensiti…

Instance-optimal Clipping for Summation Problems in the Shuffle Model of Differential Privacy
Wei Dong, Qiyao Luo, Giulia Fanti, Elaine Shi, Ke Yi
https://arxiv.org/abs/2403.10116

Instance-optimal Clipping for Summation Problems in the Shuffle Model of Differential Privacy
Differentially private mechanisms achieving worst-case optimal error bounds (e.g., the classical Laplace mechanism) are well-studied in the literature. However, when typical data are far from the worst case, \emph{instance-specific} error bounds -- which depend on the largest value in the dataset -- are more meaningful. For example, consider the sum estimation problem, where each user has an integer $x_i$ from the domain $\{0,1,\dots,U\}$ and we wish to estimate $\sum_i x_i$. This has a worst-c…