Privacy

Does that mean that other apps like signal for example have back doors?

Do criminals have a knowledge of exploits in the recommended messaging apps?

This paper investigates the feasibility of a proactive DeepFake defense framework, {\em FacePosion}, to prevent individuals from becoming victims of DeepFake videos by sabotaging face detection. The motivation stems from the reliance of most DeepFake methods on face detectors to automatically extract victim faces from videos for training or synthesis (testing). Once the face detectors malfunction, the extracted faces will be distorted or incorrect, subsequently disrupting the training or synthesis of the DeepFake model. To achieve this, we adapt various adversarial attacks with a dedicated design for this purpose and thoroughly analyze their feasibility. Based on FacePoison, we introduce {\em VideoFacePoison}, a strategy that propagates FacePoison across video frames rather than applying them individually to each frame. This strategy can largely reduce the computational overhead while retaining the favorable attack performance. Our method is validated on five face detectors, and extensive experiments against eleven different DeepFake models demonstrate the effectiveness of disrupting face detectors to hinder DeepFake generation.

This article is the best answer to people who say that they have "nothing to hide".

China’s Communist Party is stepping up the use of big data to predict people’s behavior in a bid to identify “social risks” and prevent violent attacks on members of the public in the wake of the car killings in Zhuhai earlier this month.

A New Jersey federal judge on Tuesday denied data brokers’ requests to dismiss a case brought by state law enforcement officers who sued the companies for failing to take their home addresses and phone numbers off the web.

India’s telecommunications regulator has rolled out rules designed to protect the country’s critical infrastructure networks from cyberthreats, but experts warn that the new guidelines have inadequate safeguards for users' fundamental privacy rights.

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Abstract

Browser fingerprinting is a growing technique for identifying and tracking users online without traditional methods like cookies. This paper gives an overview by examining the various fingerprinting techniques and analyzes the entropy and uniqueness of the collected data. The analysis highlights that browser fingerprinting poses a complex challenge from both technical and privacy perspectives, as users often have no control over the collection and use of their data. In addition, it raises significant privacy concerns as users are often tracked without their knowledge or consent.

Methods of Browser Fingerprinting

• A. HTTP Header Attributes
• B. Enumeration of Browser Plugins
• C. Canvas Fingerprinting
• D. WebGL Fingerprinting
• E. Audio Fingerprinting
• F. Font Fingerprinting
• G. Screen Fingerprinting
• H. WebRTC Fingerprinting
• I. CSS Fingerprinting
• J. Additional JavaScript Attributes
• K. Advanced Techniques Using Machine Learning

Italy’s data privacy regulator on Friday announced that it has levied a €5 million ($5.2 million) fine against an Italian GPS-based food delivery service for tracking the geolocation of its drivers, including outside of working hours.

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Chinese black market operators are openly recruiting government agency insiders, paying them for access to surveillance data and then reselling it online—no questions asked.