Pixel Privacy

Critics argue the new social media trend might be a tactic to access personal photos for AI training, risking user privacy. Activists warn that users lose control of their photos

The key to addressing these challenges lies in separating the encoder and decoder components of multimodal machine learning models.

In the shadow of tech overreach and endless tracking, a quiet revolution rises—one where wisdom, awareness, and dignity are reclaiming the digital realm—thanks to AI.

In today's digital landscape, organizations face the dual challenge of driving technological innovation while safeguarding user privacy. Amber Chowdhary, a notable figure in the field of AI and data privacy,

The new options would allow Bluesky users to determine how their information is used for generative AI and advertising.

Recent data from Surfshark, a well-known VPN provider, uncovered that Google Gemini is the most data-intensive AI chatbot app. DeepSeek, however, comes in fifth out of the 10 most popular applications.

In this rapidly growing digital era, privacy-preserving machine learning (PPML) is revolutionizing data-driven applications by enabling organizations to harness vast datasets while ensuring user privacy.

The privacy risks posed by generative AI are very real. From increased surveillance and exposure to more effective phishing and vishing campaigns than ever, generative AI erodes privacy en masse, indiscriminately,