PrivacyBench: A Conversational Benchmark for Evaluating Privacy in Personalized AI
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📝 Original Info
- Title: PrivacyBench: A Conversational Benchmark for Evaluating Privacy in Personalized AI
- ArXiv ID: 2512.24848
- Date: 2025-12-31
- Authors: Srija Mukhopadhyay, Sathwik Reddy, Shruthi Muthukumar, Jisun An, Ponnurangam Kumaraguru
📝 Abstract
PrivacyBench provides a critical resource for pushing the boundaries of personalized generation, enabling research into systems that are not only accurate but also temporally adaptive, contextually aware, and respectful of social contexts.📄 Full Content
The rapid advancement of Artificial Intelligence (AI), specifically Large Language Models (LLMs), has catalyzed a transition towards hyper-personalization, enabling AI assistants to leverage a user's entire digital footprint for tailored responses and actions [33,36]. Emerging agentic systems, such as Pin AI or Notion AI autonomously execute tasks and interact with users' online environments. While integrating these agents into daily workflows offers substantial potential to improve societal productivity and individual empowerment, it inevitably grants them access to highly sensitive personal data. Consequently, the central challenge for the next generation of responsible web assistants lies in reconciling the immense utility of personalization with the ethical imperative of preserving user privacy [28,34].
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