Privacy

In my first weeks as Executive Director of EFF, I’ve been reminded every day how consequential this moment is in determining what kind of future we will have. We are on the edge. What each one of us steps up to do – with our expertise, energy, and resources – will determine whether our future is one of openness, security, and fundamental rights, or one controlled through fear, surveillance, and centralized power. I am proud to take the torch and help lead our EFF community forward at this pivota

WASHINGTON, D.C. — Last night, federal lawmakers introduced the People-First Chatbot Act, a clear framework to make chatbots safer for everyone and address the harms caused by AI chatbots that were rushed into the public’s hands with little oversight or transparency. EPIC applauds Reps. Valerie Foushee and Greg Casar for sponsoring this important bill.

WASHINGTON, D.C. — Last night, federal lawmakers introduced the People-First Chatbot Act, a clear framework to make chatbots safer for everyone and address the harms caused by AI chatbots that were rushed into the public’s hands with little oversight or transparency. EPIC applauds Reps. Valerie Foushee and Greg Casar for sponsoring this important bill. This legislation was crafted to ensure that the companies operating AI chatbots provide users with clear disclosures, material safeguards, and co

Co-authored with Kelly Brandmeyer, FPF U.S. Policy Intern In a two-day span from June 28 to June 30, the New Jersey legislature introduced and passed A5328, amending New Jersey’s comprehensive privacy law and establishing new data broker and “data collector” registration requirements. The new data broker law has a uniquely broad scope and high financial […]

In the near future, AI -powered surveillance systems will be able to track everything we do in public, and much of what we do in private. And if we do something wrong—shoplift, litter, jaywalk, you name it—the system will notice, retain it, tie it to your official government record, communicate that fact to you, and provide real-time alerts to any relevant authorities… and maybe also to the general public. Think of these systems as automated speed cameras, but on steroids. Only they’ll enforce n

Meta says its AI glasses are an “assistant that understands the world from your perspective.” Critics say they’re “even more privacy invasive than you think.” One thing both parties can agree upon, though, is that these smart glasses are a technology that has attracted all manner of controversy. Since the 2023 release of the Ray-Ban Meta, these smart lenses have divided people. Evangelists praise the ability to take photos and videos without having to dig out their phone, as well as the navigati

Social Media + Society, Volume 12, Issue 3, July-September 2026. This article examines the role the performance-tracking app Strava plays in facilitating professional careers and engagement in a growing endurance sports industry. Through qualitative interviews with 25 elite endurance athletes and their agents, I find ...

Video anomaly detection (VAD) is critical for automated surveillance but remains fragile under challenging conditions such as illumination variations, fast motion, and complex backgrounds when relying solely on visible light videos. To address these limitations, we propose EVAD, an event enhanced VAD framework that jointly exploits conventional video and event streams captured by bio inspired event cameras. Event sensors asynchronously capture brightness changes with high temporal resolution, of

Developers' choices about what data a system collects, how it is used and shared, and what defaults govern user choices directly shape users' privacy experiences. Yet, developers often make problematic privacy-related design decisions without realizing the potential consequences. We introduce Privacy Detective, a narrative investigation game that leverages real-world legal documents to train developers' privacy awareness. In the game, players search for privacy violation evidence derived from le

This paper was accepted at the AI4TCI (Workshop on AI for Secure and Trustworthy Critical Infrastructure Systems) Workshop at the International Conference on Availability, Reliability and Security (ARES) 2026. Autonomous negotiation agents are increasingly deployed in high-stakes settings such as insurance and procurement. While cryptographic techniques protect explicitly disclosed constraint values, they fail to address a subtler threat: behavioral privacy leakage, where an adversary infers pri

Last May, EFF reported that a sheriff’s office in Texas searched data from more than 83,000 automated license plate reader (ALPR) cameras to track down a woman suspected of self-managing an abortion. ALPRs are promoted as tools for keeping communities safe by finding missing persons and locating stolen vehicles, but this case showed how ALPRS can be weaponized to investigate people’s private healthcare decisions. And these aren’t the only tools in the surveillance arsenal: others include locatio

Users are always seeking more control over their social networking experience to make it better, whether to improve privacy or enhance flexibility. Interoperability between social networking platforms like Facebook and TikTok has so many benefits that solve those issues. Say you’re on multiple platforms because you have friends you follow on different networks, but you’ve decided to choose one platform with better privacy practices. With interoperability, you could switch and still interact with

On June 10, 2026, the FPF Center for Artificial Intelligence convened a Frontiers Workshop in Washington, DC. Held as part of FPF’s National Science Foundation (NSF) and the Department of Energy (DoE)-funded Privacy-Enhancing Technologies (PETs) Research Coordination Network, the workshop brought together privacy and frontier AI practitioners to examine challenges at the intersection of data […]

LLM-based ASR adapted to regulated domains such as banking is bottlenecked by privacy: real speech is costly and legally constrained to collect, making synthetic text-to-speech (TTS) an attractive substitute. Yet synthetic speech stays acoustically mismatched with real recordings, and work on this gap has stayed within supervised fine-tuning (SFT). We instead turn to reinforcement learning, and show that Group Relative Policy Optimization (GRPO) extracts far more from the same synthetic speech t

Le Parlement européen a voté jeudi 9 juillet la prolongation de la dérogation qui autorise les grandes plateformes à surveiller volontairement les communications électroniques pour y détecter les contenus relevant d’abus sexuels sur mineurs. La demande de rejet a pourtant recueilli 314 votes favorables, soit une majorité relative. Deux jours après l’approbation de la procédure […]

Google owes its existence to the open web, but today, its technological “innovations” have much to do with locking users into a “walled garden.” The latest of these is “ reCAPTCHA Mobile Verification ,” an experimental initiative that will let companies block users if they are running independent, "de-googled" versions of Android. These “indie Android” versions are favored by people who want to protect their privacy and their attention by blocking trackers and ads. Worse, this is just the latest

Meta Platforms Inc. this week sought to reassure consumers about the privacy safeguards of its controversial artificial intelligence-enabled glasses, yet at the same time it’s reportedly pushing even creepier capabilities in its “internal prototypes.” The company’s AI-powered eyewear has a growing reputation as a creepy technology, but in a blog post Tuesday the company announced […] The post Meta reportedly testing prototype AI specs that record everything the user sees and hears appeared first

Modern Video Object Segmentation (VOS) involves tracking and segmenting user-specified targets. While recent approaches have achieved remarkable performance in single-target scenarios, extending them to multi-target settings typically involves replicating the single-target processing for each individual object, resulting in reduced frame rates (FPS) with unbounded latency as target count increases. Built upon Segment Anything 2 (SAM2), we propose SAM-MT, which addresses this by transforming the

Senior research fellow Jon Penney and co-author Bruce Schneier argue that widely deploying AI surveillance could be corrosive to democracy. The post AI Surveillance Is Being Supercharged–And It Will Chill Social Progress appeared first on The Citizen Lab .

Modern machine learning (ML) increasingly relies on complex models whose behavior is difficult to characterize beyond empirical performance metrics. Across a wide range of tasks, including prediction, generation, and decision-making, models with similar empirical performance can exhibit markedly different properties in terms of their transparency, interpretability, robustness, fairness, privacy, and certifiability. This survey highlights how optimization- and certification-oriented reasoning can

Machine learning models are increasingly deployed in high-stakes domains, raising concerns about both privacy and fairness. Differential Privacy (DP) has become a gold standard for privacy-preserving data analysis, while fairness-aware mechanisms aim to mitigate discrimination against underrepresented groups. However, these objectives can conflict: DP often amplifies disparities across demographic groups, and little is known about whether established fairness interventions remain effective under

Machine learning models are increasingly deployed in high-stakes domains, raising concerns about both privacy and fairness. Differential Privacy (DP) has become a gold standard for privacy-preserving data analysis, while fairness-aware mechanisms aim to mitigate discrimination against underrepresented groups. However, these objectives can conflict: DP often amplifies disparities across demographic groups, and little is known about whether established fairness interventions remain effective under

The tech giant said people can opt out - but privacy campaigners called it a "recipe for disaster".

Le FBI a pu arrêter un pirate en se servant d’une information délivrée par Microsoft : le GDID. Il s’agit d’un identifiant généré par Windows, spécifique à la machine et ne pouvant pas être changé simplement. En revanche, cet identifiant n’est pas pensé initialement pour la surveillance. Explications. Le département américain de la Justice (DoJ) a […]

Precise autonomous navigation on unstructured planetary surfaces is a critical prerequisite for future exploration missions, particularly in GNSS-denied environments such as the Lunar South Pole or Martian deserts. Traditional Visual Odometry (VO) methods, which rely on tracking low-level geometric features (e.g., corners), often fail under the extreme illumination contrast of the Moon or the textural monotony of the Martian regolith. In this work, we present a zero-shot semantic landmark-based

Federated Learning (FL) enables privacy-preserving model training across heterogeneous distributed systems, such as smartgrid forecasting or traffic-flow prediction from geographically dispersed sensors and devices. A key challenge in such settings is capturing client-specific patterns while addressing data heterogeneity and uncertainty at scale. Existing approaches, including Bayesian Neural Networks (BNNs) and clustering-based methods, struggle with scalability and consistent personalization.

Ransomware poses an escalating cybersecurity threat as attackers continuously modify behavioral patterns to evade static defenses. Although existing machine learning-based detectors often achieve strong predictive performance, they generally assume fixed training data and do not support the selective removal of previously learned samples. This limitation conflicts with privacy regulations such as the GDPR and CCPA, which require the removal of sensitive user data upon request. To address this ch

AI-mediated communication is increasingly being utilized to help facilitate interactions; however, in privacy sensitive domains, an AI mediator has the additional challenge of considering how to preserve privacy. In these contexts, a mediator may redact or withhold information, raising questions about how users perceive these interventions and whether explanations of system behavior can improve trust. In this work, we investigate how explanations of redaction operations can affect user trust in

In the global race to build and dominate the AI industry, it can sure seem like the interests of ordinary people sit last on the agenda. It's just the opposite for EFF. While companies furiously jam AI tools into their veins and your eyeballs, EFF’s technologists, activists, and attorneys have been meticulously cutting through the hype to ensure AI can serve your privacy and free expression. Technology has leaned into a new era, and this summer you can help EFF fight for the people. JOIN EFF Ove

Concept unlearning in text-to-image diffusion models is critical for safe and practical deployment: with rising privacy concerns, copyright disputes, trademark constraints, and safety regulations, deployed systems must be able to suppress unwanted concepts after training. Existing methods often remove the target concept effectively, but practical unlearning also requires an equally fundamental property: the unlearned model should retain quality, diversity, and semantic coverage on benign generat

Chatbots powered by generative AI (e.g., OpenAI's ChatGPT and Google's Gemini) are increasingly being appropriated for emotional support and companionship. These tools offer a suite of security and privacy (S&P) controls, including model training opt-outs and memory toggles, yet how the presence of these controls influences users' attitudes toward emotionally sensitive disclosure remains understudied. We conducted a mixed-methods vignette study with 354 U.S. participants to examine how S&P contr

Today's links How US states and international trustbusters can beat Big Tech: Their common enemies are Trump and his tech giants. Hey look at this: Delights to delectate. Object permanence: Sex work synonyms; Carthedral; French pirates; Suffragette surveillance; Hidden library apartments; "The Meaning of July the Fourth for the Negro" x James Earl Jones; Farage quits; Peak indifference; Self publishing; Pepsi spies try to buy Coke formula; Steal this wiki; SF is the only lit people care enough a

Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy. However, traditional FL frameworks rely on a centralized aggregation server and assume honest-but-curious clients, making them susceptible to both server-side inference and client-side poisoning attacks. Although recent work has explored secure and Byzantine-resilient FL protocols, they face a fundamental trade-off among privacy, integri

Under a fixed privacy budget, the utility of differentially private (DP) training is ultimately determined by its optimization efficiency. Standard first-order DP optimizers such as DP-SGD rely solely on local gradients and ignore the underlying loss curvature. This geometric blindness causes severe zigzagging in ill-conditioned landscapes, squandering precious privacy budgets on inefficient iterations. Practitioners are thus trapped in a bind: either stop training prematurely or inject massive

We present key challenges and future research directions in the security and privacy of agentic AI, based on a horizon-scanning exercise that brought together thirty leading international experts from academia, industry, and government to engage in focused discussions and collaborative exercises on the emerging risks associated with the growing agency of AI.

[The content of this article has been produced by our advertising partner.] The Radiology Department fields a steady flow of enquiries from many patients at the same time, arriving at all hours of the day and night, often stretching over days or months as patients consider their options or return after consulting their referring doctor. Each time a conversation resumes, staff have to pick up where it left off. The underlying work is complex too: 1,000+ distinct examination items, each with...

High quality data is of vital importance for unlocking the full potential of AI for end users. Villalobos et al. stated in 2024 that finding new sources of such data is getting harder as most publicly-available human generated data will soon have been used. Additionally, publicly available data often is not representative of users of a particular system — for example, a research speech dataset of contractors interacting with an AI assistant will likely be more homogeneous, well articulated and s

Autonomous negotiation agents are increasingly deployed in high-stakes settings such as insurance and procurement. While cryptographic techniques protect explicitly disclosed constraint values, they fail to address a subtler threat: behavioral privacy leakage, where an adversary infers private constraints from observable negotiation dynamics such as concession trajectories, timing, and convergence patterns. This paper investigates behavioral differential privacy in multi-round negotiation protoc