Privacy

Over the past two years, the Georgian government has built a comprehensive face recognition enforcement system, procured by a Moscow-based company with ties to the Federal Security Service (FSB). The impact on demonstrators is appalling.

Athlete assessment is a critical process for tracking physical progress and identifying elite talent. However, during mass recruitment drives, traditional methods rely on manual observation, which is inherently subjective and unscalable, or basic computer vision (CV) systems limited to quantitative repetition counting. These standard approaches lack the "coaching intelligence" required to evaluate qualitative physiological markers such as form degradation, spinal articulation, and fatigue. This

This Pride month, we’re calling on the dating app Grindr to prioritize LGBTQ+ user safety by making privacy the default across its platform. That means no more sharing personal data with advertisers or training AI on private information without users’ opt-in consent. Grindr is a dating app for the LGBTQ+ community; and for queer people, privacy violations can have life-altering consequences. Information that reveals someone’s sexual orientation, gender identity, or HIV status can be used by empl

Ignoring EFF’s warnings about the dangers and impossibility of implementing a new mandate for 3D print surveillance software , the California State Assembly has signed off on legislation to do just that. In the process, legislators amended the bill to make it even more confusing, while failing to address the risks to privacy, speech, and consumer rights. We must renew our call on legislators to drop this bill as it heads to the state senate, and protect the tools of creators in the state. Take a

Last-iterate convergence and generalization guarantees in first-order convex learning hinge on the monotonicity of the update operator. While linear averaging preserves the monotonicity of gradient updates, this property is often violated when gradients are aggregated non-affinely, as in modern pipelines enforcing constraints like adaptivity, privacy, robustness or fairness. Whether it is possible to design non-affine aggregation rules that maintain monotonicity has remained an open question. We

In this study, we present a replay-based framework for uncertainty-aware persistent tracking of multiple advected surface patches using an autonomous marine vehicle operating in spatiotemporal-varying currents. The method combines three components: local flow estimation, covariance-aware patch-boundary propagation with intermittent boundary fusion, and mission-level scheduling over multiple patches. Each patch is represented by a polygonal boundary, whose vertices are propagated through the esti

Seeking transparency and accountability in Paraguay’s use of facial recognition, EFF, the Association of Technology, Education, Development, Research, Communication (TEDIC), and the Centre for Justice and International Law (CEJIL) filed a complaint with the Inter-American Commission on Human Rights against the state for arbitrarily denying access to information about its implementation and use of the technology as a tool for mass surveillance that erodes people’s privacy rights. The case involve

Multimodal web agents can assist humans in operating repetitive GUI tasks, where effective task planning is essential for decomposing complex tasks into executable actions. While small open source MLLMs are cost efficient and privacy preserving compared with commercial large models, they suffer from weak planning and limited cross website generalization. To address these limitations, we introduce the planning experience exploration and utilization (PEEU) method, which autonomously explores envir

The Federal Communications Commission wants to require telecommunications providers to collect vast amounts of personal information from every person who wants a phone number in the name of combatting scam and spam calls. This plan will fail to combat the deluge of unwanted calls people in the United States receive every day while giving untrustworthy companies a gold mine of information that would harm everyday consumer’s privacy, access to communications, and ability to speak freely. The requi

Text-to-image (T2I) diffusion models typically require substantial computational resources and cloud infrastructure, posing significant challenges for edge deployment in terms of latency, cost, and user privacy. We present JuZhou 1.0, an ultra-lightweight T2I foundation model designed for fully offline, on-device execution. JuZhou 1.0 achieves its efficiency through four key designs: (1) a compact image-generation backbone consisting of a 0.385B-parameter denoising U-Net and a 1.90M-parameter di

When a car passes an automated license plate reader (ALPR), its plate is captured and instantly compared against a list of vehicles that police are actively looking for or that police have identified for real-time surveillance. These are called “hotlists,” and EFF has learned that one used by agencies across the country targets immigrants on behalf of Immigration and Customs Enforcement (ICE). Agencies using Flock Safety ALPR systems commonly allow the plates their cameras collect to be compared

Today's links Jailbreaking isn't theft: It wasn't progress when they did it, it's not piracy when we do it back to them. Hey look at this: Delights to delectate. Object permanence: Major AI breakthrough; Disney v Pooh tombstone; Vancouver riot kiss; Farage admits Brexit lies; Protecting the web from its founders; Sanders x Hillary; Surveillance pricing v your dollars. Upcoming appearances: Philadelphia, Chicago, London, Edinburgh, Sydney, Melbourne, Brighton, London, South Bend. Recent appearanc

Developing robust artificial intelligence models for 4D (3D + time) medical imaging is constrained by limited annotated data, inter-device domain shifts, and privacy restrictions. To address this, we propose a 4D controllable generative framework for anatomically consistent data augmentation. A semi-supervised variational autoencoder learns a compact latent representation of anatomical volumes while jointly predicting aligned segmentation masks in a unified framework. Anatomical structure is the

Large language model agents increasingly query databases, search document collections, call external APIs, remember past interactions, and act on a user's behalf. As they move from answering questions to operating over sensitive data, privacy becomes harder to enforce. An agent touches many data sources, runs multi-step workflows, keeps state across sessions, and acts with delegated permissions. Sensitive information can therefore leak not only through its final answer but through the queries it

By Paige Garvin, FPF Communications Intern The Future of Privacy Forum hosted its third annual DC Privacy Forum: Advancing Principled Data Protection, AI, and Digital Governance Practices on June 10th, 2026. This year’s Forum gathered government officials, academics, civil society representatives, and privacy professionals to discuss developments in AI governance, privacy regulation, youth online safety, […]

Sold to the public as a foreign surveillance tool, Section 702 is the law has let intelligence agencies spy on millions of Americans’ private conversations without a warrant. Despite years of revelations about this law's misuse, Congress has repeatedly reauthorized Section 702 without meaningful reform. Until this month, that is, when it finally lapsed in a major victory for privacy. In our latest EFFector newsletter , we're covering the expiration of Section 702 and what happens next . JOIN OUR

To better assist users with completing challenging tasks, AI agents mediate communications, access data, and interact with different APIs. Many employers (and even nation-states) already provide their users with this technology. However, widespread adoption of AI agents creates a new risk to abuse access to user data for another goal: surveilling users. These users might not even have the ability or permission to control the actions and data accesses of the surveilling agents. We introduce and f

Medium Access Control (MAC) address randomization has been widely adopted during the IEEE 802.11 network discovery phase as a countermeasure against passive tracking. This paper exposes vulnerabilities in these privacy protocols by demonstrating that devices remain identifiable using Machine Learning (ML)-based fingerprinting. To study the potential tracking capabilities of a passive attacker, we evaluate different eavesdropping scenarios and configurations. To this end, we extract unencrypted h

Digital surveillance does much more than steal data. It inflicts deep human wounds; it stops people from safely developing and expressing their identities, breeds trauma that can last for generations, and fractures the human mind.

Multi-agent LLM environments require robust mechanisms for shared knowledge management. This paper formalizes the fleet-memory problem and identifies four foundational failure modes: unauthorized leakage, stale propagation, contradiction persistence, and provenance collapse. To address these, we define explicit systems-level primitives: scoped retrieval, temporal supersession, provenance tracking, and policy-governed memory propagation. These primitives are implemented in MemClaw, a production m

Tensor decomposition of donor $\times$ cell-type $\times$ gene single-cell data recovers \emph{multicellular programs}: coordinated axes of inter-individual transcriptional variation that span cell types and stratify disease. Yet immune single-cell atlases are increasingly multi-institution, multi-ancestry, and governed, so patient cells often cannot be pooled. We present a federated estimator: each site computes a local program subspace, and a coordinator merges these by stacked SVD under feder

Unsupervised video object tracking aims to decompose dynamic scenes into persistent, object-centric entities without manual annotations. Many recent approaches rely on slot-based representations, where a fixed set of latent variables ("slots") represent individual objects across frames. To preserve object identity, these models enforce temporal consistency on slot embeddings. However, when appearance and pose are entangled, this consistency objective conflicts with object motion and viewpoint ch

Multi-turn tool-using agents must coordinate long-horizon tool sequences while tracking dialogue state and policy constraints. Existing approaches often separate inference-time orchestration from parameter-level learning, leaving tool selection weakly structured and preference updates vulnerable to train--deployment prompt mismatch. For within-benchmark self-improvement, ToolGraph combines schema-derived topology, transition weights estimated from successful rollouts, and history-aware controls

Federated low-rank adaptation methods are attractive for fine-tuning large models under communication and privacy constraints, but heterogeneous client data can induce geometric misalignment between local low-rank updates. We study whether this subspace misalignment leads to destructive aggregation and slower convergence in LoRA-based federated learning. We propose a subspace-regularized federated LoRA objective that encourages local client updates to remain close to a shared global reference su

AI agents acting on behalf of users are constantly making decisions, and for users to trust their agents, those decisions must align with what they actually want. Privacy is an important alignment problem for agents: every message, post, or tool call an agent makes is a contextual judgment about what is appropriate to share, with whom, and under which conditions. Because such judgments depend on social expectations and norms, human judgment does not merely label privacy violations but also helps

Public policies are being developed around the world to address privacy, economic, intellectual property, energy, and other risks that AI technologies pose. Involvement from the general public is essential to governance as an accountability and alignment mechanism. However, participating in and impacting policymaking can be challenging for sections of the public that lack extensive networks, lobbying capabilities, and other forms of power. This challenge is especially acute for marginalized comm

Autonomous agentic AI systems driven by Large Language Models (LLMs) introduce a new class of security, privacy, and compliance challenges: an agent that can invoke tools, manipulate data, install software, and coordinate with peer agents across organizational boundaries must be constrained not just by authentication and access control, but by the full structure of enterprise governance. This includes specifying what agents are permitted and prohibited from doing, what they areobliged to do afte

Workforce demand is uneven across organizations. Project-based companies may simultaneously face skill shortages in one unit while other firms hold underutilized employees with relevant expertise. Conventional hiring, contracting, and temporary agency models address parts of this problem, but they also create legal, ethical, organizational, and data-governance risks. This paper reframes a seminar project called Balanced Workforce into a governance-by-design framework for privacy-preserving inter

The advent of Generative Artificial Intelligence (GenAI), and in particular Large Language Models (LLMs), is reshaping educational practice, while intensifying ethical debate about its adoption. To date, the dominant paradigm remains cloud-based and text-only chatbot: a centralized service that offers limited pedagogical control, weak transparency over knowledge sources, and non-trivial risks for privacy and regulatory compliance. This model also presumes continuous connectivity and recurring AP

The U.S. Food and Drug Administration (FDA)'s Food Traceability Rule requires agri-food supply chain stakeholders (stakeholders)--including farmers, fishers, retail workers, and others--to maintain detailed tracking records beginning in January 2026. Through this Rule, the FDA envisions a "New Era of Tech-Enabled Traceability," in which standardized, harmonized tracking data serve as a foundational public health infrastructure, enabling more rapid identification and removal of potentially contam

Illegal, unreported, and unregulated fishing (IUU) traditionally refers to fishing activities that violate applicable laws or occur in areas that lack applicable laws. We propose the term IUU+ to capture a broader suite of fisheries sector environmental and associated supply chain trade-related crimes and behaviors. Although IUU+ activity is widely recognized as a serious threat to marine ecosystems, markets, and livelihoods, a quantitative understanding of these incidents, e.g., their frequency

Medical image classification faces a fundamental dilemma: while deep learning models achieve remarkable performance at scale, real-world clinical scenarios often suffer from severe data scarcity due to annotation costs, privacy constraints, and disease rarity. This challenge is particularly pronounced in pathological scar classification, where differentiating keloids from hypertrophic scars requires subtle expert knowledge and labeled images are extremely limited. We propose a novel paradigm tha

We propose a rigorous and systematic mathematical framework for tracking the cognitive trajectories of a user, in the context of algorithmic sycophancy and AI-driven delusional spiraling. Using tools from dynamical systems theory and stochastic differential equations, we explore how individuals perceive, interpret, and update their beliefs as they interact with AI chatbots that possess hidden traits of sycophancy. We treat the evolving conviction as a continuous log-odds state variable, coupled

Collective Adaptive Systems (CAS) increasingly rely on machine learning to let each node learn from locally sensed data, aligning its behavior with the surrounding environment. Scaling this intelligence, however, raises fundamental challenges: sensed data is often privacy-sensitive, preventing centralized collection; nodes are mobile, traversing regions where nearby nodes perceive similar phenomena while distant ones observe radically different conditions, creating natural spatial clusters; and

When distributed agents exchange text across organizational boundaries, privacy leakage arises not only from explicit identifiers but also from distributional signatures such as formatting conventions, vocabulary choices, and syntactic patterns. We propose DiSan(Disentangled Sanitization), a privacy-preserving sanitization framework and a built-in component of Intern-Shannon for multi-agent collaboration. DiSan uses a two-stream encoder to factorize text into a source-invariant role subspace tha

Graph-based recommender systems are highly effective at extracting collaborative signals from user--item interactions, and federated learning (FL) allows these models to be trained while preserving user privacy. However, aggregating graph representations across distributed, non-IID clients remains a challenge; structural embeddings learned locally often misalign, and naive averaging fails to capture meaningful cross-client relationships. Most existing federated graph methods rely exclusively on

Proactive warning is an important capability for edge intelligent services, where the system predicts whether a subject will successfully complete an incoming task under strict latency and privacy constraints. Such prediction depends on both long-term static attributes and short-term dynamic states derived from historical interaction logs. Recent Large Language Models (LLMs) offer strong long-context reasoning for constructing structured profiles from these logs, but existing solutions face two

Interfacing Artificial Intelligence (AI) with democracy is one of the most profound challenges of our times. On the one hand, AI comes with opportunities to overcome long-standing challenges in democracy, such as low participation in deliberative and voting processes with poor representation of people. On the other hand, new risks arise from AI algorithms that are privacy-intrusive, biased, manipulative, spread misinformation and influence election results. Moving beyond the over-simplistic ques

Moving instance segmentation (MIS) attracts increasing attention due to its broad applications in traffic surveillance, autonomous driving, and animal tracking. Event cameras record asynchronous brightness changes, providing high temporal resolution and dynamic range, which makes them highly sensitive to motion information. By fusing event and image features, motion cues from events can complement spatial details from images, enhancing the performance of MIS. However, current multimodal MIS meth