Search: “anonymization”

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CategoryAll Ransomware 693 Malware 686 Vulnerability 577 Policy & legal 566 Data breach 480 Exploit / PoC 398 Threat actor 360 Industry 274 Phishing & fraud 262 Research 133 AI safety & security 36 AI tools & infra 31 Advisory 19 Tools 17

SeverityAll critical 1489 high 1221 medium 694 low 1059 info 99

Differential Trust: Dynamic Multi-Authority Anonymous Credentials with Epoch-Weighted Updates

Researchers propose MA-ACEW, the first multi-authority anonymous credential model with epoch-weighted issuance and efficient cross-epoch credential updates.

The paper introduces MA-ACEW, a multi-authority anonymous credential scheme that weights authorities differently during credential issuance, targeting decentralized systems such as Proof-of-Stake networks. Its core primitive, Epoch-Bound Pointcheval-Sanders Signatures (EB-PS), binds signatures to time epochs, enabling non-interactive credential updates when authority weight distributions change. The authors formalize EUF-eCMA unforgeability and prove unforgeability, anonymity, and blindness under a novel STB-GPS assumption. Aggregating a credential from 128 partial credentials takes about 10.68 ms on average.

arXiv cs.CRSep 16, 15:21 UTC · 18h agoResearch

I Am No One: Style-Aware Paraphrasing for Text Anonymization

Prompt-driven style-aware paraphrasing with LLMs cuts authorship attribution F1 by 60-70% while preserving content quality.

The paper proposes a style-aware, prompt-driven anonymization approach using pretrained LLMs to build compact stylistic profiles from minimal samples and rewrite text to suppress identifiable style markers while preserving meaning. It addresses stylometric re-identification risks in anonymized text, including ASR transcripts of meetings and call-center calls where leakage persists after acoustic anonymization. Across blog and review datasets, the approach reduces authorship attribution F1 by 60-70%, substantially outperforming both DP-based and non-DP baselines while maintaining readability.

arXiv cs.CRSep 11, 02:07 UTC · 6d agoResearch

Differentially Private EEG Feature Anonymization: A Privacy-Utility Case Study in Clinical Neurophysiology

Case study applies Gaussian and Laplace differential privacy to clinical EEG features, quantifying privacy-utility trade-offs across three deployment scenarios.

Researchers evaluate subject-level differential privacy for EEG-derived feature representations using Gaussian and Laplace perturbations across client-side, centralized server-side, and decentralized local training scenarios. Utility is assessed with statistical measures and a downstream machine-learning check on clinical neurophysiology data. Results show DP can be integrated into EEG workflows, but mechanism choice, privacy parameters, and sensitivity calibration strongly influence data utility, particularly on small and imbalanced clinical datasets. The study highlights the privacy-utility trade-off in protecting biomedical signals against re-identification and inference risks.

arXiv cs.CRSep 10, 16:31 UTC · 6d agoResearch

Sociotechnical Aspects of Tor Relay Rejection

User study and simulations of Tor's relay end-of-life rejection policy find operators favor it; network churn affects anonymity more than EoL exclusions.

The study examines the Tor Project's 2019 end-of-life policy that rejects outdated relays, which constitute a notable fraction of consensus weight. A user study of 26 relay operators found they generally view the policy favorably despite limited awareness, though operational practices occasionally exclude newly installed relays. Historical-data-driven simulations show the policy gives adversaries only marginal advantage, with network churn exerting a more pronounced effect on user anonymity. Analysis of four exclusion rounds shows a minority of rejected relays typically account for over 50% of the security provided by all excluded relays, informing EoL policy recommendations.

arXiv cs.CRSep 14, 08:12 UTC · 3d agoResearch

Topological Fraud Detection in Latent Transaction Spaces

Researchers present a privacy-preserving fraud detection method combining unsupervised filtering and supervised classification on anonymized transaction embeddings for low-latency triage.

The paper describes fraud detection performed entirely on topologically anonymized transaction embeddings. It iterates unsupervised filtering followed by supervised classification ('sniping') to flag suspicious activity. The goal is ultra-low-latency, privacy-preserving triage for institutions without exposing personally identifiable information.

arXiv cs.CRSep 8, 08:50 UTC · 9d agoResearch

Trends in Web Threats: Attackers Were More Active During Holiday Season

Unit 42 tracked 533,000 malicious landing URL incidents from October-December 2021, showing web threats peaked during the holiday shopping season.

Unit 42 detected 533,452 malicious landing URL incidents (120,753 unique) and 2,906,875 malicious host URL incidents (165,255 unique) from October through December 2021. Threat activity peaked in November, likely tied to Black Friday in the United States, United Kingdom, and Germany. Most malicious domains appeared to originate in the United States, followed by Russia and Germany. Personal sites, blogs, business sites, and shopping sites were the most common apparently benign entry points for attacks.

Palo Alto Unit 42Aug 17, 13:06 UTC · Aug 17, 2026Research

Apple Reference Image: A New Approach for Verified Photography

Apple introduces Reference Image, hardware-backed verifiable photography on iPhone 18 Pro using sensor signing and Private Cloud Compute to counter AI-generated fakes.

Apple announced Reference Image, an opt-in camera mode debuting on the main sensor of iPhone 18 Pro and iPhone 18 Pro Max that produces securely timestamped, verifiable photographs. The design splits into two phases: a secure digital negative created by cryptographically signing pixel data at the sensor immediately after capture (preventing injection or tampering), then developing that negative into a reference image. Private Cloud Compute handles processing without exposing image contents to anyone, including Apple, and fraudulent reference images can be revoked without revealing the photographer's identity. Apple positions the system as stronger than C2PA-based approaches, which sign metadata after capture, are vulnerable to editing-chain compromise, and can tie images to a device or individual.

Hacker News · securitySep 16, 02:07 UTC · 1d agoResearch HN 25↑ · 18 comments

Evaluating the NIST Bugs Framework Against CWE as a Successor for Automated Vulnerability Classification

NIST Bugs Framework evaluation shows it is more structured and automation-friendly than CWE for automated vulnerability classification, with gaps in attribute guidance.

The paper evaluates NIST SP 800-231's Bugs Framework (BF) against CWE as a target for automated CVE classification using a systematically screened corpus of CVE-to-CWE research. An inter-rater study with 2 subject-matter experts mapping 13 CVEs showed strong agreement on BF's cause and operation axes but only fair agreement on the attribute axis. Automated classification was tested across two LLM deployments under different budgets, and findings support BF as more structured and automation-friendly than CWE, though gaps include under-specified attribute guidance and missing fix commits for closed-source software.

arXiv cs.CRSep 14, 23:18 UTC · 2d agoResearch1

When the Whole Company Adopts AI: What It Does to Your SOC

Analysis of 16.9 million SOC alerts finds AI-related alerts at 0.43%, growing 685% since February, with 94.1% noise and 0.02% real attacks.

A review of roughly 16.9 million SOC alerts found about 73,000 (0.43%) were AI-related, a share that grew 685% between February and June 2026. Of AI-related alerts, 94.1% were noise, 5.8% genuine risks, and 0.02% real attacks; 79.8% received benign verdicts, 81.7% were automatically suppressed, and only 5.4% reached a human analyst. The only confirmed attacks were phishing campaigns that weaponized AI brand names as lures, while developer coding agents spawning shells and reading credential stores routinely tripped detections written before AI agents existed.

The Hacker NewsSep 12, 10:24 UTC · 4d agoResearch2

Automobile Camouflage to Hide from Flock Cameras

Schneier on Security highlights a printed vehicle-camouflage pattern tested to defeat Flock surveillance cameras and Axon body cameras.

The post discusses covering cars with printed patterns designed to fool Flock automated license-plate recognition software, with testing reportedly done against Flock and Axon body cameras. Reader comments question effectiveness against other ALPR vendors, Flock's RF MAC-address upgrade, and whether such camouflage might become regulated. The page also contains off-topic comment threads about anti-bot over-blocking and privacy.

Schneier on SecuritySep 7, 11:06 UTC · 9d agoResearch

Can your coding style predict whether your code is vulnerable?

University of Massachusetts Dartmouth researchers present VulStyle, a stylometry-based vulnerability detector that also exposes benchmark reliability problems.

VulStyle combines stylometric features with syntax-tree structure and source tokens, pre-trained on about 4.9 million functions across seven programming languages and fine-tuned on five vulnerability detection datasets. It beat token-only detectors on some benchmarks but its F1 drops sharply on DiverseVul, which the authors link to noisy labels inflating reported performance across popular datasets. The authors argue style-aware detection should be harder to evade but did not test this empirically, and they note that uniform LLM-generated code may strip away the individual developer style the model depends on.

Help Net SecurityAug 24, 04:21 UTC · 24d agoResearch1

Introducing Unit 42’s Attribution Framework

Unit 42 releases its Attribution Framework, a systematic method using Diamond Model and Admiralty scores to attribute activity clusters to named threat actors.

Palo Alto Networks' Unit 42 introduced a structured framework for threat actor attribution built on the Diamond Model of Intrusion Analysis and Admiralty reliability/credibility scoring. The framework tracks activity at three levels: activity clusters (named CL-STA, CL-CRI, CL-UNK, or CL-MIX), temporary threat groups, and named threat actors using the constellation naming schema. Analysts score evidence across TTPs, tooling, malware code, OPSEC, infrastructure, timelines, and victimology to decide when to merge or elevate clusters, avoiding premature group naming.

Palo Alto Unit 42Aug 17, 10:52 UTC · Aug 17, 2026Research