Tootfinder

The public feed of the Meta AI app is filled with private and sensitive information, suggesting users might not be aware they are sharing their chats publicly (Amanda Silberling/TechCrunch)
https://techcrunch.com/2025/06/12/the-meta-ai-app-is-a-privacy-dis…

The Meta AI app is a privacy disaster | TechCrunch
Meta does not indicate to users what their privacy settings are as they post, or where they are even posting to. So, if you log into Meta AI with Instagram, and your Instagram account is public, then so too are your searches.

The Meta AI app is a privacy disaster
It sounds like the start of a 21st-century horror film: Your browser history has been public all along, and you had no idea. That’s basically what it feels like right now on the new stand-alone Meta AI app, where swathes of people are publishing their ostensibly private conversations with the chatbot. […]
😬

The Meta AI app is a privacy disaster | TechCrunch
Meta does not indicate to users what their privacy settings are as they post, or where they are even posting to. So, if you log into Meta AI with Instagram, and your Instagram account is public, then so too are your searches.

Future of Privacy Forum Announces Annual Privacy and AI Leadership Awards
https://fpf.org/press-releases/future-of-privacy-forum-announces-annual-privacy-and-ai-leadership-awards/
@…

"Despite good-faith efforts by DSI [dense street imagery] providers to protect individual privacy through blurring faces and license plates, these measures fail to address broader privacy concerns. In this work, we find that increased data density and advancements in artificial intelligence enable harmful group membership inferences from supposedly anonymized data."
via @…

Privacy of Groups in Dense Street Imagery
Spatially and temporally dense street imagery (DSI) datasets have grown unbounded. In 2024, individual companies possessed around 3 trillion unique images of public streets. DSI data streams are only set to grow as companies like Lyft and Waymo use DSI to train autonomous vehicle algorithms and analyze collisions. Academic researchers leverage DSI to explore novel approaches to urban analysis. Despite good-faith efforts by DSI providers to protect individual privacy through blurring faces and l…

Multiverse Privacy Theory for Contextual Risks in Complex User-AI Interactions
Ece Gumusel
https://arxiv.org/abs/2506.10042 https://a…

Multiverse Privacy Theory for Contextual Risks in Complex User-AI Interactions
In an era of increasing interaction with artificial intelligence (AI), users face evolving privacy decisions shaped by complex, uncertain factors. This paper introduces Multiverse Privacy Theory, a novel framework in which each privacy decision spawns a parallel universe, representing a distinct potential outcome based on user choices over time. By simulating these universes, this theory provides a foundation for understanding privacy through the lens of contextual integrity, evolving preferenc…

Sounds like you can now pay to have all your data sent through Israeli intelligence if that’s what does it for you.
#expressVPN #VPN #israel

‼️ Neues #Risiko für unbewusste #Datenlecks bei Meta AI:
Natürlich eher relevant, wenn Ihr eine #Katze seid und nach einer Methode fragt, die

The Meta AI app is a privacy disaster | TechCrunch
Meta does not indicate to users what their privacy settings are as they post, or where they are even posting to. So, if you log into Meta AI with Instagram, and your Instagram account is public, then so too are your searches.

Vending machines at the University of Waterloo violated privacy act
https://flip.it/23eUXQ

Meta AI is a disaster.
#meta

The Meta AI app is a privacy disaster | TechCrunch
Meta does not indicate to users what their privacy settings are as they post, or where they are even posting to. So, if you log into Meta AI with Instagram, and your Instagram account is public, then so too are your searches.

An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis
Ming Wang, Zhaoyang Duan, Dong Xue, Fangzhou Liu, Zhongheng Zhang
https://arxiv.org/abs/2507.08050 https://arxiv.org/pdf/2507.08050 https://arxiv.org/html/2507.08050
arXiv:2507.08050v1 Announce Type: new
Abstract: The labor-intensive nature of medical data annotation presents a significant challenge for respiratory disease diagnosis, resulting in a scarcity of high-quality labeled datasets in resource-constrained settings. Moreover, patient privacy concerns complicate the direct sharing of local medical data across institutions, and existing centralized data-driven approaches, which rely on amounts of available data, often compromise data privacy. This study proposes a federated few-shot learning framework with privacy-preserving mechanisms to address the issues of limited labeled data and privacy protection in diagnosing respiratory diseases. In particular, a meta-stochastic gradient descent algorithm is proposed to mitigate the overfitting problem that arises from insufficient data when employing traditional gradient descent methods for neural network training. Furthermore, to ensure data privacy against gradient leakage, differential privacy noise from a standard Gaussian distribution is integrated into the gradients during the training of private models with local data, thereby preventing the reconstruction of medical images. Given the impracticality of centralizing respiratory disease data dispersed across various medical institutions, a weighted average algorithm is employed to aggregate local diagnostic models from different clients, enhancing the adaptability of a model across diverse scenarios. Experimental results show that the proposed method yields compelling results with the implementation of differential privacy, while effectively diagnosing respiratory diseases using data from different structures, categories, and distributions.
toXiv_bot_toot

No one ever seems to mention how weirdly difficult the #WashingtonPost has made it to secure one's personal #privacy on their site.
☑️ Opt Out of Sale of Personal Information

❓ for privacy-minded folks- anyone have a recommendation for a scale that measures more than weight (ex. muscle mass, fat %) but is also privacy-protecting?
And ideally has an “app” or interface that can track the info, see trends over time, etc.
Many choices online… but I have no idea where they are sending data (or to whom they are selling it).
Most private option of course is a scale that keeps all data local, but there is convenience in an app that easily tracks data, s…

How to Protest Safely in the Age of Surveillance
No Paywall
https://www.wired.com/story/how-to-protest-safely-surveillance-digital-privacy/

How to Protest Safely in the Age of Surveillance
Law enforcement has more tools than ever to track your movements and access your communications. Here’s how to protect your privacy if you plan to protest.

If you still think that #Apple cares for the #privacy of its paying customers:
Apple Gave #Governments Data on Thousands of Push

If you aren't playing with:
defaults write bundle.id.goes.here EnablePasteboardPrivacyDeveloperPreview -bool yes
on a recent macOS, you should
https://developer.apple.com/documentation/updates/appkit#macOS-pasteboard-privacy

My Personal Privacy - Your resource for achieving better online privacy
Why privacy:
The right to privacy is a well-established, fundamental human right. It is necessary for a safe and just society. It is especially important in regions where intolerance and discrimination are backed by autocratic regimes. While you may not feel the need for privacy in all of your online activity, you should be able to achieve a high level of privacy when you need it. This website and the document…

Amendments to the Montana Consumer Data Privacy Act Bring Big Changes to Big Sky Country
https://fpf.org/blog/amendments-to-the-montana-consumer-data-privacy-act-bring-big-changes-to-big-sky-country/

Enforcing Speech Content Privacy in Environmental Sound Recordings using Segment-wise Waveform Reversal
Modan Tailleur, Mathieu Lagrange, Pierre Aumond, Vincent Tourre
https://arxiv.org/abs/2507.08412

Enforcing Speech Content Privacy in Environmental Sound Recordings using Segment-wise Waveform Reversal
Environmental sound recordings often contain intelligible speech, raising privacy concerns that limit analysis, sharing and reuse of data. In this paper, we introduce a method that renders speech unintelligible while preserving both the integrity of the acoustic scene, and the overall audio quality. Our approach involves reversing waveform segments to distort speech content. This process is enhanced through a voice activity detection and speech separation pipeline, which allows for more precise…

Knockoffs Inference under Privacy Constraints
Zhanrui Cai, Yingying Fan, Lan Gao
https://arxiv.org/abs/2506.09690 https://arxiv.org/p…

Knockoffs Inference under Privacy Constraints
Model-X knockoff framework offers a model-free variable selection method that ensures finite sample false discovery rate (FDR) control. However, the complexity of generating knockoff variables, coupled with the model-free assumption, presents significant challenges for protecting data privacy in this context. In this paper, we propose a comprehensive framework for knockoff inference within the differential privacy paradigm. Our proposed method guarantees robust privacy protection while preservi…

FuboTV agrees to pay $3.4M to settle a 2023 class action lawsuit alleging it violated US and California privacy laws over user data; Fubo denies the claims (Luke Bouma/Cord Cutters News)
https://cordcuttersnews.com/fu…

Fubo May Owe You Money After Agreeing to Pay $3.4 Million to Settle Class Action Lawsuit Over Privacy Violations | Cord Cutters News
FuboTV Inc. and FuboTV Media Inc. have agreed to pay $3.4 million to settle a class action lawsuit alleging the companies violated federal and California privacy laws, including the Video Privacy Protection Act (VPPA), the California Invasion of Privacy Act (CIPA), and California Civil Code § 1799.3. The lawsuit claims Fubo improperly collected, stored, used, […]

23andMe privacy ombudsman recommends company obtains consent for sale of customer data https://therecord.media/23andme-privacy-ombudsman-recommends-consent-sale

I updated my website's list of links to add @USERNAME@WEBSITE to every social media account I own.
This way, it’s clearer what my name is and which site it belongs to. It might look like federation, but it’s not on most sites; it just works as a placeholder.
Read more here: https://midtsveen.codeberg.page

TIL about Quad9, “A public and free DNS service for a better security and privacy” https://quad9.net/
Sounds good, good as it sounds?

Quad9 | A public and free DNS service for a better security and privacy
A public and free DNS service for a better security and privacy

Me, 1.5 hours ago: “Ok, it’s gonna be hot but screw it, let’s fix the privacy screen/window & door lock in the OTHER car door this afternoon”
Me, 1.4 hours ago: “where the fuck is the part for the screen motor??”
Me, now: “godsdammit”
(still haven’t found it)

COMUNICATO STAMPA – L’Istituto Italiano per la Privacy e la Valorizzazione dei Dati lancia un chatbot powered by OpenAI sulla protezione dei dati online
https://www.

Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language Models
Elena Sofia Ruzzetti, Giancarlo A. Xompero, Davide Venditti, Fabio Massimo Zanzotto
https://arxiv.org/abs/2506.10024

Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language Models
Large Language Models (LLMs) memorize, and thus, among huge amounts of uncontrolled data, may memorize Personally Identifiable Information (PII), which should not be stored and, consequently, not leaked. In this paper, we introduce Private Memorization Editing (PME), an approach for preventing private data leakage that turns an apparent limitation, that is, the LLMs' memorization ability, into a powerful privacy defense strategy. While attacks against LLMs have been performed exploiting previou…

#OrganicMaps wurde geforkt, weil es Probleme mit der Transparenz und den Finanzen zu geben scheint. Das neue Communityprojekt heißt #CoMaps #OSM

CoMaps
A community-led fork of Organic Maps. Reinforced with commitment to transparency, privacy and being not-for-profit.

This https://arxiv.org/abs/2506.00759 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Understanding and Mitigating Cross-lingual Privacy Leakage via Language-specific and Universal Privacy Neurons
Large Language Models (LLMs) trained on massive data capture rich information embedded in the training data. However, this also introduces the risk of privacy leakage, particularly involving personally identifiable information (PII). Although previous studies have shown that this risk can be mitigated through methods such as privacy neurons, they all assume that both the (sensitive) training data and user queries are in English. We show that they cannot defend against the privacy leakage in cro…

Here you go a fork of @… know as @… is now on @… .

Did you miss today’s #MediaEdClub webinar, “Privacy, Power, and Platforms?” Check out the archived / recorded video and all related links:
https://mediaeducationlab.com/events/pri…

A well written, but terrifying read
#surveillance #privacy
https://pr…

The growing surveillance state in the U.S. is far worse than you imagined
Through legal loopholes, executive orders, and shady contracts, the Trump administration crushes safeguards protecting against surveillance

“Legitimate interests is now our legal basis for using your information to improve Meta Products.”
Alright, Zuck. 😂
Here’s what's included:
https://www.facebook.com/privacy/policy/?section_id=18.4-LegitimateInterestsWeRely

Altman's iris-scanning orbs may have us in a privacy tizzy, but let's not forget that Zuck is hoping to recoup the $60B he lost on VR by gathering data in Meta's Ray-Bans—for AI, yes, but probably also to personalize ads you won't be able to look away from https://www.

At first I thought "wow, there's a #privacy event in #Vilnius but then I read the post.

Decentralized Optimization with Amplified Privacy via Efficient Communication
Wei Huo, Changxin Liu, Kemi Ding, Karl Henrik Johansson, Ling Shi
https://arxiv.org/abs/2506.07102

Decentralized Optimization with Amplified Privacy via Efficient Communication
Decentralized optimization is crucial for multi-agent systems, with significant concerns about communication efficiency and privacy. This paper explores the role of efficient communication in decentralized stochastic gradient descent algorithms for enhancing privacy preservation. We develop a novel algorithm that incorporates two key features: random agent activation and sparsified communication. Utilizing differential privacy, we demonstrate that these features reduce noise without sacrificing…

Graph-based Gossiping for Communication Efficiency in Decentralized Federated Learning
Huong Nguyen, Hong-Tri Nguyen, Praveen Kumar Donta, Susanna Pirttikangas, Lauri Lov\'en
https://arxiv.org/abs/2506.10607

Graph-based Gossiping for Communication Efficiency in Decentralized Federated Learning
Federated learning has emerged as a privacy-preserving technique for collaborative model training across heterogeneously distributed silos. Yet, its reliance on a single central server introduces potential bottlenecks and risks of single-point failure. Decentralizing the server, often referred to as decentralized learning, addresses this problem by distributing the server role across nodes within the network. One drawback regarding this pure decentralization is it introduces communication ineff…

Secure Cooperative Gradient Coding: Optimality, Reliability, and Global Privacy
Shudi Weng
https://arxiv.org/abs/2507.07565 https://a…

Secure Cooperative Gradient Coding: Optimality, Reliability, and Global Privacy
This paper studies privacy-sensitive federated learning (FL) with unreliable communication, focusing on secure aggregation and straggler mitigation. While secure aggregation cryptographically reconstructs the global model without exposing client updates, random link failures disrupt its key coordination, degrading model accuracy. Moreover, unreliable communication can lead to objective inconsistency, causing the global model to converge to arbitrary, sub-optimal points far from the intended opt…

From Legal Text to Tech Specs: Generative AI's Interpretation of Consent in Privacy Law
Aniket Kesari, Travis Breaux, Tom Norton, Sarah Santos, Anmol Singhal
https://arxiv.org/abs/2507.04185

From Legal Text to Tech Specs: Generative AI's Interpretation of Consent in Privacy Law
Privacy law and regulation have turned to "consent" as the legitimate basis for collecting and processing individuals' data. As governments have rushed to enshrine consent requirements in their privacy laws, such as the California Consumer Privacy Act (CCPA), significant challenges remain in understanding how these legal mandates are operationalized in software. The opaque nature of software development processes further complicates this translation. To address this, we explore the use of Large…

What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
Erik Buchholz, Natasha Fernandes, David D. Nguyen, Alsharif Abuadbba, Surya Nepal, Salil S. Kanhere
https://arxiv.org/abs/2506.09312

What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
While location trajectories offer valuable insights, they also reveal sensitive personal information. Differential Privacy (DP) offers formal protection, but achieving a favourable utility-privacy trade-off remains challenging. Recent works explore deep learning-based generative models to produce synthetic trajectories. However, current models lack formal privacy guarantees and rely on conditional information derived from real data during generation. This work investigates the utility cost of e…

Understanding the Error Sensitivity of Privacy-Aware Computing
Mat\'ias Mazzanti (University of Buenos Aires), Esteban Mocskos (University of Buenos Aires), Augusto Vega (IBM T. J. Watson Research Center), Pradip Bose (IBM T. J. Watson Research Center)
https://arxiv.org/abs/2506.07957

Understanding the Error Sensitivity of Privacy-Aware Computing
Homomorphic Encryption (HE) enables secure computation on encrypted data without decryption, allowing a great opportunity for privacy-preserving computation. In particular, domains such as healthcare, finance, and government, where data privacy and security are of utmost importance, can benefit from HE by enabling third-party computation and services on sensitive data. In other words, HE constitutes the "Holy Grail" of cryptography: data remains encrypted all the time, being protected while in …

The head of the California Privacy Protection Agency on the future of data privacy regulation https://therecord.media/california-privacy-protection-agency-tom-kemp-interview

Meta found a new way to violate your privacy. Here’s what you can do.
https://www.msn.com/en-us/news/technology/meta-found-a-new-way-to-violate-your-privacy-here-s-what-you-can-do/ar-AA1GecPs

How to secure your phone before joining a protest
✅ The Protesters' Guide to Smartphone Security
https://www.privacyguides.org/articles/2025/01/23/activists-guide-securing-your-smartphone/

The Protesters' Guide to Smartphone Security
Your phone is an essential tool, but it also represents a huge risk to your privacy and security. Understanding these best practices when it comes to securing your smartphone will help keep you and your data safe.

Replaced article(s) found for cs.CC. https://arxiv.org/list/cs.CC/new/
[1/1]:
Privacy-aware Berrut Approximated Coded Computing for Federated Learning

Minoritised Ethnic People's Security and Privacy Concerns and Responses towards Essential Online Services
Aunam Quyoum, Mark Wong, Sebati Ghosh, Siamak F. Shahandashti
https://arxiv.org/abs/2506.06062

Minoritised Ethnic People's Security and Privacy Concerns and Responses towards Essential Online Services
Minoritised ethnic people are marginalised in society, and therefore at a higher risk of adverse online harms, including those arising from the loss of security and privacy of personal data. Despite this, there has been very little research focused on minoritised ethnic people's security and privacy concerns, attitudes, and behaviours. In this work, we provide the results of one of the first studies in this regard. We explore minoritised ethnic people's experiences of using essential online ser…

Intelligent Travel Activity Monitoring: Generalized Distributed Acoustic Sensing Approaches
Ruikang Zhong, Chia-Yen Chiang, Mona Jaber, Rupert De Wilde, Peter Hayward
https://arxiv.org/abs/2506.10237

Intelligent Travel Activity Monitoring: Generalized Distributed Acoustic Sensing Approaches
Obtaining data on active travel activities such as walking, jogging, and cycling is important for refining sustainable transportation systems (STS). Effectively monitoring these activities not only requires sensing solutions to have a joint feature of being accurate, economical, and privacy-preserving, but also enough generalizability to adapt to different climate environments and deployment conditions. In order to provide a generalized sensing solution, a deep learning (DL)-enhanced distribute…

Tip for #FlyingLess as a family: prepare for long train rides with board card games. The #privacy friendly app games by @… are absolutely brilliant, easy to use, stable not stealing your data (or trying to sell your kids stuff).
(Edited to give right masto account: top work, thanks Secuso🙏 for the hours of fun your sudoku app has provided already).
https://secuso.aifb.kit.edu/english/105.php

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new/
[1/1]:
Privacy-Aware Spectrum Pricing and Power Control Optimization for LEO Satellite Internet-of-Things

Why can #Zucc at #Meta just spy on everyone without their consent and illegally collect their browsing history, and when it comes to light people call it a "privacy violation" and say "I didn't have any privacy to begin with so IDC" and they just get a fine that's insigni…

Anyone know good smart speaker things like Amazon Alexa that doesn't phone home etc?
#smarthome #homeassistant #privacy

On Differential Privacy for Adaptively Solving Search Problems via Sketching
Shiyuan Feng, Ying Feng, George Z. Li, Zhao Song, David P. Woodruff, Lichen Zhang
https://arxiv.org/abs/2506.05503

On Differential Privacy for Adaptively Solving Search Problems via Sketching
Recently differential privacy has been used for a number of streaming, data structure, and dynamic graph problems as a means of hiding the internal randomness of the data structure, so that multiple possibly adaptive queries can be made without sacrificing the correctness of the responses. Although these works use differential privacy to show that for some problems it is possible to tolerate $T$ queries using $\widetilde{O}(\sqrt{T})$ copies of a data structure, such results only apply to numer…

Zuckerberg’s new Meta AI app gets personal in a very creepy way
https://www.washingtonpost.com/technology/2025/05/05/meta-ai-privacy/
(Or :

QualitEye: Public and Privacy-preserving Gaze Data Quality Verification
Mayar Elfares, Pascal Reisert, Ralf K\"usters, Andreas Bulling
https://arxiv.org/abs/2506.05908

QualitEye: Public and Privacy-preserving Gaze Data Quality Verification
Gaze-based applications are increasingly advancing with the availability of large datasets but ensuring data quality presents a substantial challenge when collecting data at scale. It further requires different parties to collaborate, therefore, privacy concerns arise. We propose QualitEye--the first method for verifying image-based gaze data quality. QualitEye employs a new semantic representation of eye images that contains the information required for verification while excluding irrelevant …

Trump is taking decisive steps to crush administrative safeguards and preempt legal challenges,
allowing his administration to rapidly consolidate a surveillance state with diminished privacy rights.
Aided by recent Supreme Court decisions, Republican lawmakers, and quickly eroding due process rights,
the administration is ensuring that the data and sensitive information of each person in the U.S. can be used against them.

The growing surveillance state in the U.S. is far worse than you imagined
Through legal loopholes, executive orders, and shady contracts, the Trump administration crushes safeguards protecting against surveillance

Palantir's Collection of Disease Data at C.D.C. Stirs Privacy Concerns (Apoorva Mandavilli/New York Times)
https://www.nytimes.com/2025/06/06/health/cdc-data-privacy-palantir.html?unlocked_article_code=1.M08.jzJB.cljQA69ueENV&smid=url-share
http://www.memeorandum.com/250606/p108#a250606p108

Rethinking the Privacy of Text Embeddings: A Reproducibility Study of "Text Embeddings Reveal (Almost) As Much As Text"
Dominykas Seputis, Yongkang Li, Karsten Langerak, Serghei Mihailov
https://arxiv.org/abs/2507.07700

Rethinking the Privacy of Text Embeddings: A Reproducibility Study of "Text Embeddings Reveal (Almost) As Much As Text"
Text embeddings are fundamental to many natural language processing (NLP) tasks, extensively applied in domains such as recommendation systems and information retrieval (IR). Traditionally, transmitting embeddings instead of raw text has been seen as privacy-preserving. However, recent methods such as Vec2Text challenge this assumption by demonstrating that controlled decoding can successfully reconstruct original texts from black-box embeddings. The unexpectedly strong results reported by Vec2…

I'll test it on Apples.
I use the Orion browser on Apple computers. EFF's attempting to tell advertisers to USE EXPLICIT METHODS to control ads, and our privacy.
The Orion browser has its own "advanced" blockers.
But because it has Firefox extensions, it should "work" in Orion (the free browser by search engine company Kagi). Also, on iPads and iPhones, extensions work in Orion, but not Safari.
Privacy is a Human Right.

Quantifying Mix Network Privacy Erosion with Generative Models
Vasilios Mavroudis, Tariq Elahi
https://arxiv.org/abs/2506.08918 https://

Quantifying Mix Network Privacy Erosion with Generative Models
Modern mix networks improve over Tor and provide stronger privacy guarantees by robustly obfuscating metadata. As long as a message is routed through at least one honest mixnode, the privacy of the users involved is safeguarded. However, the complexity of the mixing mechanisms makes it difficult to estimate the cumulative privacy erosion occurring over time. This work uses a generative model trained on mixnet traffic to estimate the loss of privacy when users communicate persistently over a per…

Please join me for today’s (July 7) free #MediaEdLab webinar, “Privacy, Power, and Platforms”
https://mediaeducationlab.com/events/privacy-power-and-platforms
We w…

Filing: OpenAI seeks to block a May 13 court order requiring it to preserve all ChatGPT logs, including deleted chats, arguing it poses a risk to users' privacy (Ashley Belanger/Ars Technica)
https://arstechnica.com/tech-policy/20

OpenAI slams court order to save all ChatGPT logs, including deleted chats
OpenAI defends privacy of hundreds of millions of ChatGPT users.

Tech to Support Older Adults and Caregivers: Five Privacy Questions for Age Tech
https://fpf.org/blog/tech-to-support-older-adults-and-caregivers-five-privacy-questions-for-age-tech/
@…

Federated Learning within Global Energy Budget over Heterogeneous Edge Accelerators
Roopkatha Banerjee, Tejus Chandrashekar, Ananth Eswar, Yogesh Simmhan
https://arxiv.org/abs/2506.10413

Federated Learning within Global Energy Budget over Heterogeneous Edge Accelerators
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, optimizing both energy efficiency and model accuracy remains a challenge, given device and data heterogeneity. Further, sustainable AI through a global energy budget for FL has not been explored. We propose a novel optimization problem for client selection in FL that maximizes the model accuracy within an overall energy limit and reduces training time. We solve this wi…

02:13—Good Night #Fediverse! 😴💤
I added some more autistic flare at #Night

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
Zhaoxuan Kan, Husheng Han, Shangyi Shi, Tenghui Hua, Hang Lu, Xiaowei Li, Jianan Mu, Xing Hu
https://arxiv.org/abs/2506.10399

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
Graph Convolutional Neural Networks (GCNs) have gained widespread popularity in various fields like personal healthcare and financial systems, due to their remarkable performance. Despite the growing demand for cloud-based GCN services, privacy concerns over sensitive graph data remain significant. Homomorphic Encryption (HE) facilitates Privacy-Preserving Machine Learning (PPML) by allowing computations to be performed on encrypted data. However, HE introduces substantial computational overhea…

This https://arxiv.org/abs/2506.06062 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Minoritised Ethnic People's Security and Privacy Concerns and Responses towards Essential Online Services
Minoritised ethnic people are marginalised in society, and therefore at a higher risk of adverse online harms, including those arising from the loss of security and privacy of personal data. Despite this, there has been very little research focused on minoritised ethnic people's security and privacy concerns, attitudes, and behaviours. In this work, we provide the results of one of the first studies in this regard. We explore minoritised ethnic people's experiences of using essential online ser…

Please join me on July 7th for next month's MediaEd Club webinar for the Media Education Lab: "Privacy, Power, and Platforms"
https://mediaeducationlab.com/index.php/events/privacy-power-and-platforms
If you missed today's / this month…

I nearly gave my credit card to a legit-looking Maine business, until I noticed their “privacy policy” linked to Wikipedia. That’s not laziness but AI-generated HTML vibecoded into place without review. And a lot more can go wrong than a missing legal page.
https://www.

Disclosure Audits for LLM Agents
Saswat Das, Jameson Sandler, Ferdinando Fioretto
https://arxiv.org/abs/2506.10171 https://arxiv.org/…

Disclosure Audits for LLM Agents
Large Language Model agents have begun to appear as personal assistants, customer service bots, and clinical aides. While these applications deliver substantial operational benefits, they also require continuous access to sensitive data, which increases the likelihood of unauthorized disclosures. This study proposes an auditing framework for conversational privacy that quantifies and audits these risks. The proposed Conversational Manipulation for Privacy Leakage (CMPL) framework, is an iterati…

NASA is looking for a CISO.
https://www.usajobs.gov/job/839985100?mod=djemCybersecruityPro&tpl=cs

Chief Information Security Officer (CISO)/Senior Agency Information Security Officer (SAISO)
The incumbent serves as the Chief Information Security Officer (CISO) and Senior Agency Information Security Officer (SAISO) leading the cybersecurity and privacy strategy and program implementation to protect NASA's missions and enterprise. Responsibilities include establishing strategy, policies, programs, and frameworks for the security of classified and unclassified information and information systems at NASA. Also, manages the Cybersecurity and Privacy Division within the Office of the CIO.

Gaze3P: Gaze-Based Prediction of User-Perceived Privacy
Mayar Elfares, Pascal Reisert, Ralf K\"usters, Andreas Bulling
https://arxiv.org/abs/2507.00596

Gaze3P: Gaze-Based Prediction of User-Perceived Privacy
Privacy is a highly subjective concept and perceived variably by different individuals. Previous research on quantifying user-perceived privacy has primarily relied on questionnaires. Furthermore, applying user-perceived privacy to optimise the parameters of privacy-preserving techniques (PPT) remains insufficiently explored. To address these limitations, we introduce Gaze3P -- the first dataset specifically designed to facilitate systematic investigations into user-perceived privacy. Our datas…

SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
Yan Zhou, Bradley Malin, Murat Kantarcioglu
https://arxiv.org/abs/2506.01907 https://

SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
Privacy-preserving data publication, including synthetic data sharing, often experiences trade-offs between privacy and utility. Synthetic data is generally more effective than data anonymization in balancing this trade-off, however, not without its own challenges. Synthetic data produced by generative models trained on source data may inadvertently reveal information about outliers. Techniques specifically designed for preserving privacy, such as introducing noise to satisfy differential priva…

Added a link to nsf-iaa.org with some autistic flair: #Anarchism #Syndicalism

Non-Asymptotic Analysis of Online Local Private Learning with SGD
Enze Shi, Jinhan Xie, Bei Jiang, Linglong Kong, Xuming He
https://arxiv.org/abs/2507.07041

Non-Asymptotic Analysis of Online Local Private Learning with SGD
Differentially Private Stochastic Gradient Descent (DP-SGD) has been widely used for solving optimization problems with privacy guarantees in machine learning and statistics. Despite this, a systematic non-asymptotic convergence analysis for DP-SGD, particularly in the context of online problems and local differential privacy (LDP) models, remains largely elusive. Existing non-asymptotic analyses have focused on non-private optimization methods, and hence are not applicable to privacy-preservin…

Epass: Efficient and Privacy-Preserving Asynchronous Payment on Blockchain
Weijie Wang, Jinwen Liang, Chuan Zhang, Ximeng Liu, Liehuang Zhu, Song Guo
https://arxiv.org/abs/2506.09387

Epass: Efficient and Privacy-Preserving Asynchronous Payment on Blockchain
Buy Now Pay Later (BNPL) is a rapidly proliferating e-commerce model, offering consumers to get the product immediately and defer payments. Meanwhile, emerging blockchain technologies endow BNPL platforms with digital currency transactions, allowing BNPL platforms to integrate with digital wallets. However, the transparency of transactions causes critical privacy concerns because malicious participants may derive consumers' financial statuses from on-chain asynchronous payments. Furthermore, th…

Slides for today's #MediaEdLab #MediaEdClub webinar on "Privacy, Power and Platforms"
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Privacy, Power, and Platforms - MediaEd Club (7 July 2025) by wesfryer.com
Privacy, Power, and Platforms 7 July 2025 Facilitated by Wes Fryer mediaeducationlab.com/mediaed-club/upcoming-meetings

I agree with Luke Smith: we don’t actually need social media, the internet, or even computers to survive. Living below your means and simplifying your life is not only possible but straightforward if you choose to do it.
If you really care about Privacy, don't use the Internet!
https://peertube.wtf/w/wuw…

TRIDENT -- A Three-Tier Privacy-Preserving Propaganda Detection Model in Mobile Networks using Transformers, Adversarial Learning, and Differential Privacy
Al Nahian Bin Emran, Dhiman Goswami, Md Hasan Ullah Sadi, Sanchari Das
https://arxiv.org/abs/2506.05421

TRIDENT -- A Three-Tier Privacy-Preserving Propaganda Detection Model in Mobile Networks using Transformers, Adversarial Learning, and Differential Privacy
The proliferation of propaganda on mobile platforms raises critical concerns around detection accuracy and user privacy. To address this, we propose TRIDENT - a three-tier propaganda detection model implementing transformers, adversarial learning, and differential privacy which integrates syntactic obfuscation and label perturbation to mitigate privacy leakage while maintaining propaganda detection accuracy. TRIDENT leverages multilingual back-translation to introduce semantic variance, charact…

An Architecture for Privacy-Preserving Telemetry Scheme
Kenneth Odoh
https://arxiv.org/abs/2507.06350 https://arxiv.org/pdf/2507.0635…

An Architecture for Privacy-Preserving Telemetry Scheme
We present a privacy-preserving telemetry aggregation scheme. Our underlying frequency estimation routine works within the framework of differential privacy. The design philosophy follows a client-server architecture. Furthermore, the system uses a local differential privacy scheme where data gets randomized on the client before submitting the request to the resource server. This scheme allows for data analysis on de-identified data by carefully adding noise to prevent re-identification attacks…

The Connecticut Data Privacy Act Gets an Overhaul (Again)
https://fpf.org/blog/the-connecticut-data-privacy-act-gets-an-overhaul-again/
@…

“Our republic is worth fighting for.”
Patrick G. Eddington
“Resist and Heal,” my friends. Citizenship is not a spectator sport. “Finding and sharing trusted voices” is a critical part of the anti-fascist work to which we are now called.
More on: https://

Resist and Heal on Substack
[PODCAST] How Trump is Using the Surveillance State Against Us (w/ Patrick G. Eddington) by #ReImagingLiberty https://pca.st/episode/bd03d667-195d-4b68-b70b-b1250f44d3a5 #book2read: The Triumph of Fear: Domestic Surveillance and Political Repression from McKinley to Eisenhower by Patrick G. Eddington https://a.co/d/9MyvSpM #surveillance #LawEnforcement #police #privacy #QuietSkies #SilentPartner #ethics #WatchList #ConCW

Big Bird: Privacy Budget Management for W3C's Privacy-Preserving Attribution API
Pierre Tholoniat, Alison Caulfield, Giorgio Cavicchioli, Mark Chen, Nikos Goutzoulias, Benjamin Case, Asaf Cidon, Roxana Geambasu, Mathias L\'ecuyer, Martin Thomson
https://arxiv.org/abs/2506.05290

Big Bird: Privacy Budget Management for W3C's Privacy-Preserving Attribution API
Privacy-preserving advertising APIs like Privacy-Preserving Attribution (PPA) are designed to enhance web privacy while enabling effective ad measurement. PPA offers an alternative to cross-site tracking with encrypted reports governed by differential privacy (DP), but current designs lack a principled approach to privacy budget management, creating uncertainty around critical design decisions. We present Big Bird, a privacy budget manager for PPA that clarifies per-site budget semantics and in…

Layered, Overlapping, and Inconsistent: A Large-Scale Analysis of the Multiple Privacy Policies and Controls of U.S. Banks
Lu Xian, Van Tran, Lauren Lee, Meera Kumar, Yichen Zhang, Florian Schaub
https://arxiv.org/abs/2507.05415

Layered, Overlapping, and Inconsistent: A Large-Scale Analysis of the Multiple Privacy Policies and Controls of U.S. Banks
Privacy policies are often complex. An exception is the two-page standardized notice that U.S. financial institutions must provide under the Gramm-Leach-Bliley Act (GLBA). However, banks now operate websites, mobile apps, and other services that involve complex data sharing practices that require additional privacy notices and do-not-sell opt-outs. We conducted a large-scale analysis of how U.S. banks implement privacy policies and controls in response to GLBA; other federal privacy policy requ…

Annual DC Privacy Forum: Convening Top Voices in Governance in the Digital Age
https://fpf.org/blog/annual-dc-privacy-forum-convening-top-voices-in-governance-in-the-digital-age/
@…

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning
Rami Darwish, Mahmoud Abdelsalam, Sajad Khorsandroo, Kaushik Roy
https://arxiv.org/abs/2507.07258

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning
As IoT ecosystems continue to expand across critical sectors, they have become prominent targets for increasingly sophisticated and large-scale malware attacks. The evolving threat landscape, combined with the sensitive nature of IoT-generated data, demands detection frameworks that are both privacy-preserving and resilient to data heterogeneity. Federated Learning (FL) offers a promising solution by enabling decentralized model training without exposing raw data. However, standard FL algorithm…

FPF Unveils Paper on State Data Minimization Trends
https://fpf.org/blog/fpf-unveils-paper-on-state-data-minimization-trends/
@…

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix
Jintao Guo, Ying Zhou, Chao Li, Guixun Luo
https://arxiv.org/abs/2507.06508

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix
When analyzing connection patterns within graphs, subgraph counting serves as an effective and fundamental approach. Edge-local differential privacy (edge-LDP) and shuffle model have been employed to achieve subgraph counting under a privacy-preserving situation. Existing algorithms are plagued by high time complexity, excessive download costs, low accuracy, or dependence on trusted third parties. To address the aforementioned challenges, we propose the Noisy Adjacency Matrix (NAM), which combi…

LDP$^3$: An Extensible and Multi-Threaded Toolkit for Local Differential Privacy Protocols and Post-Processing Methods
Berkay Kemal Balioglu, Alireza Khodaie, Mehmet Emre Gursoy
https://arxiv.org/abs/2507.05872

LDP$^3$: An Extensible and Multi-Threaded Toolkit for Local Differential Privacy Protocols and Post-Processing Methods
Local differential privacy (LDP) has become a prominent notion for privacy-preserving data collection. While numerous LDP protocols and post-processing (PP) methods have been developed, selecting an optimal combination under different privacy budgets and datasets remains a challenge. Moreover, the lack of a comprehensive and extensible LDP benchmarking toolkit raises difficulties in evaluating new protocols and PP methods. To address these concerns, this paper presents LDP$^3$ (pronounced LDP-C…

Unveiling Privacy Policy Complexity: An Exploratory Study Using Graph Mining, Machine Learning, and Natural Language Processing
Vijayalakshmi Ramasamy, Seth Barrett, Gokila Dorai, Jessica Zumbach
https://arxiv.org/abs/2507.02968

Unveiling Privacy Policy Complexity: An Exploratory Study Using Graph Mining, Machine Learning, and Natural Language Processing
Privacy policy documents are often lengthy, complex, and difficult for non-expert users to interpret, leading to a lack of transparency regarding the collection, processing, and sharing of personal data. As concerns over online privacy grow, it is essential to develop automated tools capable of analyzing privacy policies and identifying potential risks. In this study, we explore the potential of interactive graph visualizations to enhance user understanding of privacy policies by representing p…

Beyond Personalization: Federated Recommendation with Calibration via Low-rank Decomposition
Jundong Chen, Honglei Zhang, Haoxuan Li, Chunxu Zhang, Zhiwei Li, Yidong Li
https://arxiv.org/abs/2506.09525

Beyond Personalization: Federated Recommendation with Calibration via Low-rank Decomposition
Federated recommendation (FR) is a promising paradigm to protect user privacy in recommender systems. Distinct from general federated scenarios, FR inherently needs to preserve client-specific parameters, i.e., user embeddings, for privacy and personalization. However, we empirically find that globally aggregated item embeddings can induce skew in user embeddings, resulting in suboptimal performance. To this end, we theoretically analyze the user embedding skew issue and propose Personalized Fe…

SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks
Kaiyuan Zhang, Siyuan Cheng, Hanxi Guo, Yuetian Chen, Zian Su, Shengwei An, Yuntao Du, Charles Fleming, Ashish Kundu, Xiangyu Zhang, Ninghui Li
https://arxiv.org/abs/2506.10424

SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks
Large language models (LLMs) have achieved remarkable success and are widely adopted for diverse applications. However, fine-tuning these models often involves private or sensitive information, raising critical privacy concerns. In this work, we conduct the first comprehensive study evaluating the vulnerability of fine-tuned LLMs to membership inference attacks (MIAs). Our empirical analysis demonstrates that MIAs exploit the loss reduction during fine-tuning, making them highly effective in re…

PrivTru: A Privacy-by-Design Data Trustee Minimizing Information Leakage
Lukas Gehring, Florian Tschorsch
https://arxiv.org/abs/2506.06124 https://

PrivTru: A Privacy-by-Design Data Trustee Minimizing Information Leakage
Data trustees serve as intermediaries that facilitate secure data sharing between independent parties. This paper offers a technical perspective on Data trustees, guided by privacy-by-design principles. We introduce PrivTru, an instantiation of a data trustee that provably achieves optimal privacy properties. Therefore, PrivTru calculates the minimal amount of information the data trustee needs to request from data sources to respond to a given query. Our analysis shows that PrivTru minimizes i…

The Impact of Event Data Partitioning on Privacy-aware Process Discovery
Jungeun Lim, Stephan A. Fahrenkrog-Petersen, Xixi Lu, Jan Mendling, Minseok Song
https://arxiv.org/abs/2507.06008

The Impact of Event Data Partitioning on Privacy-aware Process Discovery
Information systems support the execution of business processes. The event logs of these executions generally contain sensitive information about customers, patients, and employees. The corresponding privacy challenges can be addressed by anonymizing the event logs while still retaining utility for process discovery. However, trading off utility and privacy is difficult: the higher the complexity of event log, the higher the loss of utility by anonymization. In this work, we propose a pipeline …

When Better Features Mean Greater Risks: The Performance-Privacy Trade-Off in Contrastive Learning
Ruining Sun, Hongsheng Hu, Wei Luo, Zhaoxi Zhang, Yanjun Zhang, Haizhuan Yuan, Leo Yu Zhang
https://arxiv.org/abs/2506.05743

When Better Features Mean Greater Risks: The Performance-Privacy Trade-Off in Contrastive Learning
With the rapid advancement of deep learning technology, pre-trained encoder models have demonstrated exceptional feature extraction capabilities, playing a pivotal role in the research and application of deep learning. However, their widespread use has raised significant concerns about the risk of training data privacy leakage. This paper systematically investigates the privacy threats posed by membership inference attacks (MIAs) targeting encoder models, focusing on contrastive learning framew…

Differentially Private Explanations for Clusters
Amir Gilad, Tova Milo, Kathy Razmadze, Ron Zadicario
https://arxiv.org/abs/2506.05900 https://

Differentially Private Explanations for Clusters
The dire need to protect sensitive data has led to various flavors of privacy definitions. Among these, Differential privacy (DP) is considered one of the most rigorous and secure notions of privacy, enabling data analysis while preserving the privacy of data contributors. One of the fundamental tasks of data analysis is clustering , which is meant to unravel hidden patterns within complex datasets. However, interpreting clustering results poses significant challenges, and often necessitates an…

WatchWitch: Interoperability, Privacy, and Autonomy for the Apple Watch
Nils Rollshausen, Alexander Heinrich, Matthias Hollick, Jiska Classen
https://arxiv.org/abs/2507.07210

WatchWitch: Interoperability, Privacy, and Autonomy for the Apple Watch
Smartwatches such as the Apple Watch collect vast amounts of intimate health and fitness data as we wear them. Users have little choice regarding how this data is processed: The Apple Watch can only be used with Apple's iPhones, using their software and their cloud services. We are the first to publicly reverse-engineer the watch's wireless protocols, which led to discovering multiple security issues in Apple's proprietary implementation. With WatchWitch, our custom Android reimplementation, we…

Human-Centered Interactive Anonymization for Privacy-Preserving Machine Learning: A Case for Human-Guided k-Anonymity
Sri Harsha Gajavalli
https://arxiv.org/abs/2507.04104

Human-Centered Interactive Anonymization for Privacy-Preserving Machine Learning: A Case for Human-Guided k-Anonymity
Privacy-preserving machine learning (ML) seeks to balance data utility and privacy, especially as regulations like the GDPR mandate the anonymization of personal data for ML applications. Conventional anonymization approaches often reduce data utility due to indiscriminate generalization or suppression of data attributes. In this study, we propose an interactive approach that incorporates human input into the k-anonymization process, enabling domain experts to guide attribute preservation based…

Post-Processing in Local Differential Privacy: An Extensive Evaluation and Benchmark Platform
Alireza Khodaie, Berkay Kemal Balioglu, Mehmet Emre Gursoy
https://arxiv.org/abs/2507.05875

Post-Processing in Local Differential Privacy: An Extensive Evaluation and Benchmark Platform
Local differential privacy (LDP) has recently gained prominence as a powerful paradigm for collecting and analyzing sensitive data from users' devices. However, the inherent perturbation added by LDP protocols reduces the utility of the collected data. To mitigate this issue, several post-processing (PP) methods have been developed. Yet, the comparative performance of PP methods under diverse settings remains underexplored. In this paper, we present an extensive benchmark comprising 6 popular L…

Aim High, Stay Private: Differentially Private Synthetic Data Enables Public Release of Behavioral Health Information with High Utility
Mohsen Ghasemizade, Juniper Lovato, Christopher M. Danforth, Peter Sheridan Dodds, Laura S. P. Bloomfield, Matthew Price, Team LEMURS, Joseph P. Near
https://arxiv.org/abs/2507.02971

Aim High, Stay Private: Differentially Private Synthetic Data Enables Public Release of Behavioral Health Information with High Utility
Sharing health and behavioral data raises significant privacy concerns, as conventional de-identification methods are susceptible to privacy attacks. Differential Privacy (DP) provides formal guarantees against re-identification risks, but practical implementation necessitates balancing privacy protection and the utility of data. We demonstrate the use of DP to protect individuals in a real behavioral health study, while making the data publicly available and retaining high utility for downst…

Empowering Manufacturers with Privacy-Preserving AI Tools: A Case Study in Privacy-Preserving Machine Learning to Solve Real-World Problems
Xiaoyu Ji, Jessica Shorland, Joshua Shank, Pascal Delpe-Brice, Latanya Sweeney, Jan Allebach, Ali Shakouri
https://arxiv.org/abs/2507.01808

Empowering Manufacturers with Privacy-Preserving AI Tools: A Case Study in Privacy-Preserving Machine Learning to Solve Real-World Problems
Small- and medium-sized manufacturers need innovative data tools but, because of competition and privacy concerns, often do not want to share their proprietary data with researchers who might be interested in helping. This paper introduces a privacy-preserving platform by which manufacturers may safely share their data with researchers through secure methods, so that those researchers then create innovative tools to solve the manufacturers' real-world problems, and then provide tools that execu…

Efficient Unlearning with Privacy Guarantees
Josep Domingo-Ferrer, Najeeb Jebreel, David S\'anchez
https://arxiv.org/abs/2507.04771 https://

Efficient Unlearning with Privacy Guarantees
Privacy protection laws, such as the GDPR, grant individuals the right to request the forgetting of their personal data not only from databases but also from machine learning (ML) models trained on them. Machine unlearning has emerged as a practical means to facilitate model forgetting of data instances seen during training. Although some existing machine unlearning methods guarantee exact forgetting, they are typically costly in computational terms. On the other hand, more affordable methods d…