Tootfinder

Password compromise is no joke;
it leads to account compromise and that leads to, well, the compromise of most everything you hold dear in this technological-centric world.
It’s why Google is telling billions of users to replace their passwords with more secure passkeys.
It’s why the FBI is warning people not to click on links in SMS messages.
It’s why stolen passwords are up for sale, in their millions, on the dark web to anyone with the very little amount of cas…

16 Billion Apple, Facebook, Google And Other Passwords Leaked — Act Now
Change all your account passwords now — don’t wait: 16 billion credentials are confirmed as having been leaked.

[Economist] Patrick Artus: 'In the US, attacks on the public research system and universities compromise growth prospects'
https://www.lemonde.fr/en/economy/article/2025/06/21/patrick-artus-in-the-us-attacks-on-the-public-research-system-and-universities-compromise-growth-prospects_6742589_19.html

How spyware, posing as an app from a humanitarian organization, spread among Syrian army officers via Telegram, and fueled the collapse of the Assad regime (Mobile Hacker)
https://www.mobile-hacker.com/2025/06/05/an…

Analysis of Spyware That Helped to Compromise a Syrian Army from Within
This case demonstrates that effective smartphone espionage doesn't always require expensive zero-day exploits or the development of sophisticated, custom and undetected spyware. Instead, attackers can achieve significant intelligence gains using older, off-the-shelf tools like Android SpyMax—especially when combined

This https://arxiv.org/abs/2502.14479 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework
The lifetime behaviour of loans is notoriously difficult to model, which can compromise a bank's financial reserves against future losses, if modelled poorly. Therefore, we present a data-driven comparative study amongst three techniques in modelling a series of default risk estimates over the lifetime of each loan, i.e., its term-structure. The behaviour of loans can be described using a nonstationary and time-dependent semi-Markov model, though we model its elements using a multistate regress…

Quantum-Resistant Domain Name System: A Comprehensive System-Level Study
Juyoul Lee, Sanzida Hoque, Abdullah Aydeger, Engin Zeydan
https://arxiv.org/abs/2506.19943

Quantum-Resistant Domain Name System: A Comprehensive System-Level Study
The Domain Name System (DNS) plays a foundational role in Internet infrastructure, yet its core protocols remain vulnerable to compromise by quantum adversaries. As cryptographically relevant quantum computers become a realistic threat, ensuring DNS confidentiality, authenticity, and integrity in the post-quantum era is imperative. In this paper, we present a comprehensive system-level study of post-quantum DNS security across three widely deployed mechanisms: DNSSEC, DNS-over-TLS (DoT), and DN…

Evangelical dogma says Christ changes you. Does that include your core beliefs? And how does Catholic dogma affect a new Washington law?
https://www.bobmuellerwriter.com/beyond-dogma-can-compassion-exist-without-compromise/

Beyond Dogma: Can Compassion Exist Without Compromise? – Bob Mueller
Evangelical dogma says Christ changes you. What about your core beliefs? And how does Catholic dogma affect a new Washington law?

Safe Pruning LoRA: Robust Distance-Guided Pruning for Safety Alignment in Adaptation of LLMs
Shuang Ao, Yi Dong, Jinwei Hu, Sarvapali Ramchurn
https://arxiv.org/abs/2506.18931

Safe Pruning LoRA: Robust Distance-Guided Pruning for Safety Alignment in Adaptation of LLMs
Fine-tuning Large Language Models (LLMs) with Low-Rank Adaptation (LoRA) enhances adaptability while reducing computational costs. However, fine-tuning can compromise safety alignment, even with benign data, increasing susceptibility to harmful outputs. Existing safety alignment methods struggle to capture complex parameter shifts, leading to suboptimal safety-utility trade-offs. To address this issue, we propose Safe Pruning LoRA (SPLoRA), a novel pruning-based approach that selectively remove…

Simulation of a closed-loop dc-dc converter using a physics-informed neural network-based model
Marc-Antoine Coulombe, Maxime Berger, Antoine Lesage-Landry
https://arxiv.org/abs/2506.19178

Simulation of a closed-loop dc-dc converter using a physics-informed neural network-based model
The growing reliance on power electronics introduces new challenges requiring detailed time-domain analyses with fast and accurate circuit simulation tools. Currently, commercial time-domain simulation software are mainly relying on physics-based methods to simulate power electronics. Recent work showed that data-driven and physics-informed learning methods can increase simulation speed with limited compromise on accuracy, but many challenges remain before deployment in commercial tools can be …

fake moderates are like "no kings? lets compromise and just have 1"

No War With Iran Will Erase Israel's Crimes in Gaza. Don't Get Distracted.
Israel has adopted a worldview in which every problem should be solved through force, and the people of Israel have become a collective that admires force and brutality while scorning dialogue and compromise.

Hackers impersonating US government compromise email account of prominent Russia researcher https://therecord.media/keir-giles-russia-researcher-email-hacked

NetSenseML: Network-Adaptive Compression for Efficient Distributed Machine Learning
Yisu Wang, Xinjiao Li, Ruilong Wu, Huangxun Chen, Dirk Kutscher
https://arxiv.org/abs/2506.16235

NetSenseML: Network-Adaptive Compression for Efficient Distributed Machine Learning
Training large-scale distributed machine learning models imposes considerable demands on network infrastructure, often resulting in sudden traffic spikes that lead to congestion, increased latency, and reduced throughput, which would ultimately affect convergence times and overall training performance. While gradient compression techniques are commonly employed to alleviate network load, they frequently compromise model accuracy due to the loss of gradient information. This paper introduces N…

Koobaatoo Asparagus – Trinity of Wisdom
#noise

LDI: Localized Data Imputation
Soroush Omidvartehrani, Davood Rafiei
https://arxiv.org/abs/2506.16616 https://arxiv.org/pdf/2506.1661…

LDI: Localized Data Imputation
Missing values are a common challenge in real-world tabular data and can significantly impair downstream analysis. While Large Language Models (LLMs) have recently shown promise in data imputation, existing methods often rely on broad, unfiltered prompts that compromise accuracy, scalability, and explainability. We introduce LDI (Localized Data Imputation), a novel framework that improves both the accuracy and transparency of LLM-based imputation by selecting a compact, contextually relevant su…

Threshold Displacement Energies of Oxygen in YBa$_2$Cu$_3$O$_7$: A Multi-Physics Analysis
Ashley Dickson, Mark R. Gilbert, Duc Nguyen-Manh, Samuel T. Murphy
https://arxiv.org/abs/2506.17976

Threshold Displacement Energies of Oxygen in YBa$_2$Cu$_3$O$_7$: A Multi-Physics Analysis
Neutron bombardment of high temperature superconducting (HTS) magnets may compromise the integrity of the magnetic confinement in future fusion reactors. The amount of damage produced by a single neutron can be predicted from the threshold displacement energies (TDE) of the constituent ions in the HTS materials, such as the Rare Earth Cuperates. Therefore, in this work a Multiphysics simulation approach is adopted to determine the threshold displacement energies for oxygen in YBa2Cu3O7. Classic…

Beyond Dogma: Can Compassion Exist Without Compromise?
I discuss a religious perspective on compassion, questioning the validity of core beliefs that might hinder empathy. Then I discuss new legislation in Washington State on clergy reporting child abuse, scrutinizing the Catholic Church's position on confessions.

Beyond Dogma: Can Compassion Exist Without Compromise? – Bob Mueller
Evangelical dogma says Christ changes you. What about your core beliefs? And how does Catholic dogma affect a new Washington law?

Probe before You Talk: Towards Black-box Defense against Backdoor Unalignment for Large Language Models
Biao Yi, Tiansheng Huang, Sishuo Chen, Tong Li, Zheli Liu, Zhixuan Chu, Yiming Li
https://arxiv.org/abs/2506.16447

Probe before You Talk: Towards Black-box Defense against Backdoor Unalignment for Large Language Models
Backdoor unalignment attacks against Large Language Models (LLMs) enable the stealthy compromise of safety alignment using a hidden trigger while evading normal safety auditing. These attacks pose significant threats to the applications of LLMs in the real-world Large Language Model as a Service (LLMaaS) setting, where the deployed model is a fully black-box system that can only interact through text. Furthermore, the sample-dependent nature of the attack target exacerbates the threat. Instead …

Ethical concerns take a back seat to practical or economic concerns only if a majority of people and power decide they do.
Accepting an ethical compromise is never a given. It is always a choice. Even if it's a choice that gets made by someone else (billionaires, CEOs, political donors, VCs, etc.).
If they're making a choice you don't like, it's time to do something about it.

It's frustrating how many important services and channels of communication rely on questionable tech. For example, my neighbourhood uses Meta's WhatsApp to the point where if I hadn't accessed the chat I wouldn't have had an ETA on reactivation of our hot water that was out for several days. For this purpose, I've made a slight compromise (using a different phone number). But I'd like to #AskFedi

Transformed Diffusion-Wave fPINNs: Enhancing Computing Efficiency for PINNs Solving Time-Fractional Diffusion-Wave Equations
Jing Li, Zhengqi Zhang
https://arxiv.org/abs/2506.11518

Transformed Diffusion-Wave fPINNs: Enhancing Computing Efficiency for PINNs Solving Time-Fractional Diffusion-Wave Equations
We propose transformed Diffsuion-Wave fractional Physics-Informed Neural Networks (tDWfPINNs) for efficiently solving time-fractional diffusion-wave equations with fractional order $α\in(1,2)$. Conventional numerical methods for these equations often compromise the mesh-free advantage of Physics-Informed Neural Networks (PINNs) or impose high computational costs when computing fractional derivatives. The proposed method avoids first-order derivative calculations at quadrature points by introdu…

This https://arxiv.org/abs/2506.04116 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging
In medical imaging, 4D MRI enables dynamic 3D visualization, yet the trade-off between spatial and temporal resolution requires prolonged scan time that can compromise temporal fidelity--especially during rapid, large-amplitude motion. Traditional approaches typically rely on registration-based interpolation to generate intermediate frames. However, these methods struggle with large deformations, resulting in misregistration, artifacts, and diminished spatial consistency. To address these chall…

A Neural Network-aided Low Complexity Chase Decoder for URLLC
Enrico Testi, Enrico Paolini
https://arxiv.org/abs/2506.10513 https://a…

A Neural Network-aided Low Complexity Chase Decoder for URLLC
Ultra-reliable low-latency communications (URLLC) demand decoding algorithms that simultaneously offer high reliability and low complexity under stringent latency constraints. While iterative decoding schemes for LDPC and Polar codes offer a good compromise between performance and complexity, they fall short in approaching the theoretical performance limits in the typical URLLC short block length regime. Conversely, quasi-ML decoding schemes for algebraic codes, like Chase-II decoding, exhibit …

Insights from Designing Context-Aware Meal Preparation Assistance for Older Adults with Mild Cognitive Impairment (MCI) and Their Care Partners
Szeyi Chan, Jiachen Li, Siman Ao, Yufei Wang, Ibrahim Bilau, Brian Jones, Eunhwa Yang, Elizabeth D Mynatt, Xiang Zhi Tan
https://arxiv.org/abs/2506.05663…

Insights from Designing Context-Aware Meal Preparation Assistance for Older Adults with Mild Cognitive Impairment (MCI) and Their Care Partners
Older adults with mild cognitive impairment (MCI) often face challenges during meal preparation, such as forgetting ingredients, skipping steps, or leaving appliances on, which can compromise their safety and independence. Our study explores the design of context-aware assistive technologies for meal preparation using a user-centered iterative design process. Through three iterative phases of design and feedback, evolving from low-tech lightbox to a digital screen, we gained insights into manag…

BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies
Kourosh Shahnazari, Seyed Moein Ayyoubzadeh, Mohammadali Keshtparvar
https://arxiv.org/abs/2506.00328

BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies
The quest for interpretable reinforcement learning is a grand challenge for the deployment of autonomous decision-making systems in safety-critical applications. Modern deep reinforcement learning approaches, while powerful, tend to produce opaque policies that compromise verification, reduce transparency, and impede human oversight. To address this, we introduce BASIL (Best-Action Symbolic Interpretable Learning), a systematic approach for generating symbolic, rule-based policies via online ev…

This https://arxiv.org/abs/2405.08719 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However, recent work has demonstrated that model misspecification can compromise the reliability of SBI, preventing its adoption in important applications where only misspecified simulators are available. This work introduces robust posterior estimation~(RoPE), a framework that overcomes model misspecification with a small re…

SafeCOMM: What about Safety Alignment in Fine-Tuned Telecom Large Language Models?
Aladin Djuhera, Swanand Ravindra Kadhe, Farhan Ahmed, Syed Zawad, Holger Boche, Walid Saad
https://arxiv.org/abs/2506.00062

SafeCOMM: What about Safety Alignment in Fine-Tuned Telecom Large Language Models?
Fine-tuning large language models (LLMs) for telecom tasks and datasets is a common practice to adapt general-purpose models to the telecom domain. However, little attention has been paid to how this process may compromise model safety. Recent research has shown that even benign fine-tuning can degrade the safety alignment of LLMs, causing them to respond to harmful or unethical user queries. In this paper, we investigate this issue for telecom-tuned LLMs using three representative datasets fea…

Information-guided optimization of image-based sensorless adaptive optics methods
Biwei Zhang, Martin J. Booth, Qi Hu
https://arxiv.org/abs/2506.07482 http…

Information-guided optimization of image-based sensorless adaptive optics methods
Adaptive optics (AO) are reconfigurable devices that compensate for wavefront distortions or aberrations in optical systems such as microscopes, telescopes and ophthalmoscopes. Aberrations have detrimental effects that can reduce imaging quality and compromise scientific information. Sensorless AO methods were introduced to correct aberrations without a separate wavefront sensor, inferring wavefront-related information directly from phase-diverse sample images. Most sensorless AO control system…

This https://arxiv.org/abs/2505.19755 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

One Model to Rank Them All: Unifying Online Advertising with End-to-End Learning
Modern industrial advertising systems commonly employ Multi-stage Cascading Architectures (MCA) to balance computational efficiency with ranking accuracy. However, this approach presents two fundamental challenges: (1) performance inconsistencies arising from divergent optimization targets and capability differences between stages, and (2) failure to account for advertisement externalities - the complex interactions between candidate ads during ranking. These limitations ultimately compromise s…

AI-Generated Compromises for Coalition Formation
Eyal Briman, Ehud Shapiro, Nimrod Talmon
https://arxiv.org/abs/2506.06837 https://ar…

AI-Generated Compromises for Coalition Formation
The challenge of finding compromises between agent proposals is fundamental to AI subfields such as argumentation, mediation, and negotiation. Building on this tradition, Elkind et al. (2021) introduced a process for coalition formation that seeks majority-supported proposals preferable to the status quo, using a metric space where each agent has an ideal point. A crucial step in this process involves identifying compromise proposals around which agent coalitions can unite. How to effectively f…

This https://arxiv.org/abs/2405.00295 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Proof of Sampling: A Nash Equilibrium-Based Verification Protocol for Decentralized Systems
This paper introduces the Proof of Sampling (PoSP) protocol, a Nash Equilibrium-based verification mechanism, and its application to decentralized machine learning inference through spML. Our protocol has a pure strategy Nash Equilibrium, compelling rational participants to act honestly. It economically disincentivizes dishonest behavior, making it costly for participants to compromise the network's integrity. In our spML protocol, we apply PoSP to decentralized inference for AI applications vi…

Tady: A Neural Disassembler without Structural Constraint Violations
Siliang Qin, Fengrui Yang, Hao Wang, Bolun Zhang, Zeyu Gao, Chao Zhang, Kai Chen
https://arxiv.org/abs/2506.13323

Tady: A Neural Disassembler without Structural Constraint Violations
Disassembly is a crucial yet challenging step in binary analysis. While emerging neural disassemblers show promise for efficiency and accuracy, they frequently generate outputs violating fundamental structural constraints, which significantly compromise their practical usability. To address this critical problem, we regularize the disassembly solution space by formalizing and applying key structural constraints based on post-dominance relations. This approach systematically detects widespread e…

SSIMBaD: Sigma Scaling with SSIM-Guided Balanced Diffusion for AnimeFace Colorization
Junpyo Seo (Department of Computer Science, Seoul National University), Hanbin Koo (Department of Computer Science, Seoul National University), Jieun Yook (Department of Computer Science, Seoul National University), Byung-Ro Moon (Department of Computer Science, Seoul National University)
…

SSIMBaD: Sigma Scaling with SSIM-Guided Balanced Diffusion for AnimeFace Colorization
We propose a novel diffusion-based framework for automatic colorization of Anime-style facial sketches. Our method preserves the structural fidelity of the input sketch while effectively transferring stylistic attributes from a reference image. Unlike traditional approaches that rely on predefined noise schedules - which often compromise perceptual consistency -- our framework builds on continuous-time diffusion models and introduces SSIMBaD (Sigma Scaling with SSIM-Guided Balanced Diffusion). …

Design Didn't Prevent These Hazard Exposures
David. E Mertz (Fermilab)
https://arxiv.org/abs/2506.03280 https://arxiv.org/pdf/250…

Design Didn't Prevent These Hazard Exposures
Two recent electrical incidents demonstrate how the design of equipment encouraged electrical workers to take actions that violated NFPA 70E principles. The features that encouraged non-compliant work execution will be described, as well as how the equipment was improved to facilitate safe work practices. Design-stage processes that help identify features that will foster rather than compromise safe work practices will be identified as well.

Reduced-weight near-cloaks for underwater invisibility
Davide Enrico Quadrelli, Sebastiano Cominelli, Gabriele Cazzulani, Francesco Braghin
https://arxiv.org/abs/2506.07593

Reduced-weight near-cloaks for underwater invisibility
Limiting the total weight of an acoustic cloak is of fundamental importance in underwater applications, where buoyancy of the cloaked object is desirable. Unfortunately, it is well known that traditional cloaking strategies imply either a mass tending to infinity or a total weight equal to the Archimedes' force, thus making a perfect cloak that preserves the buoyancy of the target impossible. In this paper, we discuss strategies to reduce the weight of the cloak seeking a good compromise betwee…

RADAR: Benchmarking Language Models on Imperfect Tabular Data
Ken Gu, Zhihan Zhang, Kate Lin, Yuwei Zhang, Akshay Paruchuri, Hong Yu, Mehran Kazemi, Kumar Ayush, A. Ali Heydari, Maxwell A. Xu, Girish Narayanswamy, Yun Liu, Ming-Zher Poh, Yuzhe Yang, Mark Malhotra, Shwetak Patel, Hamid Palangi, Xuhai Xu, Daniel McDuff, Tim Althoff, Xin Liu
https://

RADAR: Benchmarking Language Models on Imperfect Tabular Data
Language models (LMs) are increasingly being deployed to perform autonomous data analyses. However, their data awareness -- the ability to recognize, reason over, and appropriately handle data artifacts such as missing values, outliers, and logical inconsistencies -- remains underexplored. These artifacts are especially common in real-world tabular data and, if mishandled, can significantly compromise the validity of analytical conclusions. To address this gap, we present RADAR, a benchmark for…

Uncovering Reliable Indicators: Improving IoC Extraction from Threat Reports
Evangelos Froudakis, Athanasios Avgetidis, Sean Tyler Frankum, Roberto Perdisci, Manos Antonakakis, Angelos Keromytis
https://arxiv.org/abs/2506.11325

Uncovering Reliable Indicators: Improving IoC Extraction from Threat Reports
Indicators of Compromise (IoCs) are critical for threat detection and response, marking malicious activity across networks and systems. Yet, the effectiveness of automated IoC extraction systems is fundamentally limited by one key issue: the lack of high-quality ground truth. Current extraction tools rely either on manually extracted ground truth, which is labor-intensive and costly, or on automated ground truth creation methods that include non-malicious artifacts, leading to inflated false po…

Probabilistic Assessment of Engineered Timber Reusability after Moisture Exposure
Yiping Meng, Chulin Jiang, Courtney Jayne Scurr, Farzad Pour Rahimian, David Hughes
https://arxiv.org/abs/2506.11061

Probabilistic Assessment of Engineered Timber Reusability after Moisture Exposure
Engineered timber is pivotal to low-carbon construction, but moisture uptake during its service life can compromise structural reliability and impede reuse within a circular economy model. Despite growing interest, quantitative standards for classifying the reusability of moisture-exposed timber are still lacking. This study develops a probabilistic framework to determine the post-exposure reusability of engineered timber. Laminated specimens were soaked to full saturation, dried to 25% moistur…

A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging
Xuanru Zhou, Jiarun Liu, Shoujun Yu, Hao Yang, Cheng Li, Tao Tan, Shanshan Wang
https://arxiv.org/abs/2506.04116

A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging
In medical imaging, 4D MRI enables dynamic 3D visualization, yet the trade-off between spatial and temporal resolution requires prolonged scan time that can compromise temporal fidelity--especially during rapid, large-amplitude motion. Traditional approaches typically rely on registration-based interpolation to generate intermediate frames. However, these methods struggle with large deformations, resulting in misregistration, artifacts, and diminished spatial consistency. To address these chall…

Tady: A Neural Disassembler without Structural Constraint Violations
Siliang Qin, Fengrui Yang, Hao Wang, Bolun Zhang, Zeyu Gao, Chao Zhang, Kai Chen
https://arxiv.org/abs/2506.13323

Tady: A Neural Disassembler without Structural Constraint Violations
Disassembly is a crucial yet challenging step in binary analysis. While emerging neural disassemblers show promise for efficiency and accuracy, they frequently generate outputs violating fundamental structural constraints, which significantly compromise their practical usability. To address this critical problem, we regularize the disassembly solution space by formalizing and applying key structural constraints based on post-dominance relations. This approach systematically detects widespread e…

From IOCs to Group Profiles: On the Specificity of Threat Group Behaviors in CTI Knowledge Bases
Aakanksha Saha, Martina Lindorfer, Juan Caballero
https://arxiv.org/abs/2506.10645

From IOCs to Group Profiles: On the Specificity of Threat Group Behaviors in CTI Knowledge Bases
Indicators of Compromise (IOCs) such as IP addresses, file hashes, and domain names are commonly used for threat detection and attribution. However, IOCs tend to be short-lived as they are easy to change. As a result, the cybersecurity community is shifting focus towards more persistent behavioral profiles, such as the Tactics, Techniques, and Procedures (TTPs) and the software used by a threat group. However, the distinctiveness and completeness of such behavioral profiles remain largely unexp…

AURA: A Multi-Agent Intelligence Framework for Knowledge-Enhanced Cyber Threat Attribution
Nanda Rani, Sandeep Kumar Shukla
https://arxiv.org/abs/2506.10175

AURA: A Multi-Agent Intelligence Framework for Knowledge-Enhanced Cyber Threat Attribution
Effective attribution of Advanced Persistent Threats (APTs) increasingly hinges on the ability to correlate behavioral patterns and reason over complex, varied threat intelligence artifacts. We present AURA (Attribution Using Retrieval-Augmented Agents), a multi-agent, knowledge-enhanced framework for automated and interpretable APT attribution. AURA ingests diverse threat data including Tactics, Techniques, and Procedures (TTPs), Indicators of Compromise (IoCs), malware details, adversarial to…

StealthInk: A Multi-bit and Stealthy Watermark for Large Language Models
Ya Jiang, Chuxiong Wu, Massieh Kordi Boroujeny, Brian Mark, Kai Zeng
https://arxiv.org/abs/2506.05502

StealthInk: A Multi-bit and Stealthy Watermark for Large Language Models
Watermarking for large language models (LLMs) offers a promising approach to identifying AI-generated text. Existing approaches, however, either compromise the distribution of original generated text by LLMs or are limited to embedding zero-bit information that only allows for watermark detection but ignores identification. We present StealthInk, a stealthy multi-bit watermarking scheme that preserves the original text distribution while enabling the embedding of provenance data, such as userID…

This https://arxiv.org/abs/2406.02463 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Click Without Compromise: Online Advertising Measurement via Per User Differential Privacy
Online advertising is a cornerstone of the Internet ecosystem, with advertising measurement playing a crucial role in optimizing efficiency. Ad measurement entails attributing desired behaviors, such as purchases, to ad exposures across various platforms, necessitating the collection of user activities across these platforms. As this practice faces increasing restrictions due to rising privacy concerns, safeguarding user privacy in this context is imperative. Our work is the first to formulate …

This https://arxiv.org/abs/2501.10639 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Latent-space adversarial training with post-aware calibration for defending large language models against jailbreak attacks
Ensuring safety alignment has become a critical requirement for large language models (LLMs), particularly given their widespread deployment in real-world applications. However, LLMs remain susceptible to jailbreak attacks, which exploit system vulnerabilities to bypass safety measures and generate harmful outputs. Although numerous defense mechanisms based on adversarial training have been proposed, a persistent challenge lies in the exacerbation of over-refusal behaviors, which compromise the…