Privacy

Publication date: 2008 Source: Computer Law & Security Report, Volume 24, Issue 6 Author(s): Harry Small, Helen Kemmitt, Julie Wood, Ben Smith, Nick Wloch

Publication date: 2008 Source: Computer Law & Security Report, Volume 24, Issue 6 Author(s): Roger Baker

Publication date: 2008 Source: Computer Law & Security Report, Volume 24, Issue 5 Author(s): Kate Brimsted

Publication date: 2008 Source: Computer Law & Security Report, Volume 24, Issue 5 Author(s): Mark Turner, Tim Gunn

Publication date: 2008 Source: Computer Law & Security Report, Volume 24, Issue 5 Author(s): Harry Small, Michael Dizon, Rachael Jolley, Matthew Hope, Katie Cullinan

Publication date: December 2026 Source: Computers and Education: Artificial Intelligence, Volume 11 Author(s): William Villegas-Ch, Alexandra Maldonado Navarro, Jaime Govea, Joselin Garcia-Ortiz, Diego Buenaño-Fernandez

Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Lucas Kohnke, Di Zou, Chun Lai, Mingyue Michelle Gu

Societal & User-Centered Privacy in AI (SUPA) Workshop [Seattle, WA, USA] [Aug 10, 2025 - Aug 10, 2025]

This is a current list of where and when I am scheduled to speak: I’m speaking (virtually) at the Policy-Relevant Privacy Research Workshop in Calgary, Canada, on Monday, July 20, 2026. I’m speaking at Boston Leadership Exchange in Boston, Massachusetts, USA, on Wednesday, July 22, 2026. I’m speaking at Cognitive Security Conference in Las Vegas, Nevada, USA. The conference runs August 6-7, 2026; my speaking time is TBD. I’m speaking at DEF CON 34 in Las Vegas, Nevada, USA. The conventions runs

Falling detection is vital for elderly care and intelligent surveillance; however, prevailing vision-based approaches predominantly frame it as static pose classification or discrete temporal pattern matching, fundamentally overlooking the instability dynamics of the human support system. This paper proposes a physics-informed falling detection framework that recasts falling as a stability-loss event in a coupled dynamical system. We introduce a novel dual-LTC architecture comprising a Center-of

This study introduces a unified control framework for fixed-wing unmanned aerial vehicles (UAVs) fitted with a pan-tilt (PT) camera, intended to perform an end-to-end mission spanning from initial target detection to accurate terminal engagement. The proposed system employs a three-phase strategy: a vision-based target acquisition phase, an NMPC-based tracking phase, and a terminal guidance phase. During tracking, the framework uses an Unscented Kalman Filter (UKF) to fuse YOLO-based visual dete

YouTube works well with default settings, but I tighten some privacy controls and enable a few features to make it even better.

In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconstruction attacks. We show that extensive exposure can persist without rote memorization and is instead caused by a tunable connection to adversarial robustness. We begin by presenting three surprising results: (1) recent defenses that inhibit reconstruction by Model Inversion Attacks (MIAs), which evaluate leakage under an idealized attacker, do n

Efforts to bring AI glasses to a broader demographic via a partnership with Kylie Jenner appear to have backfired

arXiv:2607.11127v1 Announce Type: cross Abstract: Organizations and regulators increasingly consult large language models (LLMs) for regulatory-compliance questions, yet a wrong statutory citation can silently propagate into legal advice, compliance documentation, and policy decisions. We introduce a bilingual benchmark of 120 questions probing whether freely accessible LLMs fabricate article citations for two data-protection instruments: the EU General Data Protection Regulation (GDPR) and the

As a pivotal technique in smart healthcare, medical image fusion integrates complementary functional and structural information to facilitate accurate diagnosis and enhance clinical decision-making reliability. However, existing centralized methods typically raise serious data privacy concerns, while standard distributed approaches often fail to balance global generalization with local node personalization due to data heterogeneity. To address this, we propose FLMMIF, a privacy-preserving framew

The Los Angeles police department did not renew its contract with Flock due to data privacy concerns.

Thermal infrared (TIR) imaging is essential for UAV swarm operations in visually degraded environments. However, tracking tiny UAVs remains challenging due to limited appearance cues, frequent occlusions, and rapid maneuvers. Despite significant progress driven by benchmarks such as the Anti-UAV challenge, existing methods primarily prioritize accuracy while overlooking the computational constraints of real-time edge deployment. The standard Kalman Filter (KF) offers the efficiency required for

EPIC submitted testimony on Monday to the D.C. Council Committee on Public Works & Operations urging them to further strengthen B26-0670, the DC Government Data Privacy and Protection Act of 2026.

Large Language Models (LLMs) can make exploratory search more efficient but may undermine the reflection and iterative sensemaking needed in unfamiliar domains. Existing LLM tools often prioritize rapid answers over supporting users in tracking how their understanding evolves and how well their strategies align with their goals. We present TrailLM, a system that helps users reconstruct and revisit their exploration paths to support reflection and metacognitive engagement during information seeki

Large Language Models (LLMs) are rapidly moving from research settings into the wild, deployed on enterprise infrastructure, personal devices, and edge platforms. While cloud deployments offer scalable compute, concerns over data sovereignty, compliance, latency, and third-party dependence are driving organizations toward edge and on-premise LLMs. This shift introduces new security and privacy challenges: limited compute and memory force aggressive optimizations, including quantization, pruning,

WhatsApp usernames may reduce phone number exposure, but in India, they also raise a deeper question...

This article was originally published by IAPP linked here. Organizations using scraped data for AI training should prepare for heightened expectations around data minimization, transparency and accountability. On 7 July, the European Data Protection Board approved “Guidelines on web scraping in the context of generative AI.” Perhaps not surprisingly, the EDPB considers that web scraping [...] The post Thought for the week: Web scraping for generative AI is subject to the GDPR appeared first on C

A public resource tracking all the legal challenges to the Trump administration's executive orders and actions. The post Litigation Tracker: Legal Challenges to Trump Administration Actions appeared first on Just Security .

The China Internet Association released a self-regulatory pact on personal information protection for AI agents at a forum in Beijing, with Baidu, Tencent, Alibaba, Volcengine, and 27 other internet companies among the first signatories. The pact is aimed at standardizing how AI agents collect, process, and use personal data as agent-based services spread across internet […]

Decision support systems (DSS) increasingly run retention what-if analysis on synthetic customer populations, because privacy constraints preclude unrestricted use of real data. Such a system is trustworthy only if the synthetic data lead managers to the same decisions as the real data would; yet prevailing criteria certify distributional similarity, not decision alignment, so a synthetic population can match every marginal distribution while still steering a marketing team toward the wrong camp

State regulators are ramping up children's privacy enforcement, focusing on parental consent, company knowledge of minors, and protective safeguards.

Artificial intelligence agents can both read data and write actions, meaning security and governance are critically important.

Large language models (LLMs) based agents are beginning to participate in portfolio construction and market analysis, where decisions must be justified under evolving information and risk constraints. Current assessment practice, however, remains poorly aligned with this setting: many studies rely on static examinations or report only terminal portfolio returns, while the intermediate evidence, analyst judgments, and execution steps that produced those returns stay largely invisible. We introduc

The Indian startup ecosystem continued to mature in FY26, with 22 new-age tech companies making their public market debut as…

Agentic research systems are emerging as a new paradigm for coordinating scientific workflows beyond isolated model inference, code generation, or statistical analysis. However, deployment in institutional biomedical environments requires governed mechanisms for research planning, data access, workflow orchestration, evidence tracking, reproducibility, and human oversight. We present NVAITC AI Scientist (NAIS), a governed end-to-end agentic research system designed to support domain-general scie

Senior China correspondent Amy Hawkins on China’s embrace of AI, from medical avatars to food delivery drones and state surveillance While the spread of AI has been met perhaps with a lot of scepticism in the west, China has fully embraced the technology, explains Amy Hawkins , from millions of users talking to AI doctors, to the use of intelligent robots in factories, and drones delivering food on the Great Wall of China. AI has also been eagerly taken up by the state, not least in the opportun

A former Mayo Clinic research director claims she was silenced, demoted and ultimately fired for sounding the alarm on AI safety and patient privacy lapses at the health system. Traci Tamiko Eto is now suing Mayo for retaliation. The post Former Mayo Clinic Leader Sues System Over Alleged AI Cover-Up: 6 Things to Know appeared first on MedCity News .

Following privacy backlash.

Redefining the data protection supply chain.

The accelerating shift toward low-carbon power systems, together with the widespread adoption of behind-the-meter technologies such as rooftop solar and electric vehicles, is placing new operational and analytical demands on electricity grids. At the same time, smart-grid research increasingly relies on machine learning (ML), yet progress is constrained by limited access to high-resolution household energy data due to privacy concerns, regulatory barriers, and collection costs. This work present

Editor's note: This report has been updated to clarify details related to the Sora video generator. Facebook and Instagram parent company Meta took down a new artificial intelligence model from Instagram on Friday, just days after debuting this feature to immediate public backlash. The company advertised its Muse Image AI model on Tuesday as a...

Users and Hollywood agencies raised privacy and copyright concerns about the new tool, Muse Image.

Meta was criticised for feature launched on Tuesday that automatically lets users generate images using content from public Instagram accounts Meta has said ⁠it is discontinuing an AI feature launched this week that allowed users to generate images using public Instagram ⁠accounts, after drawing widespread ⁠criticism over ​privacy concerns, including from a Hollywood union. “Our intent was to provide a useful creative tool and to give people control ⁠over whether their public content could be re