IDP-Bench: Benchmarking ability of LLMs to protect personal information in interdependent privacy contexts
arXiv:2606.09908v1 Announce Type: cross Abstract: Large language models (LLMs) are becoming widely deployed as personal AI assistants with access to sensitive user data, making privacy a major challenge for their design and evaluation. Prior work focuses mainly on individual-level risks, overlooking \textbf{interdependent privacy (IDP)}--where one person's data may be revealed by others without their knowledge or consent. We address this gap by introducing \textbf{IDP-Bench}: the first LLM benchmark for IDP scenarios, grounded in the Contextual Integrity (CI) framework. We evaluate eight open-
The increasing deployment of autonomous LLMs as personal AI assistants necessitates a deeper understanding and evaluation of privacy beyond individual consent, particularly as these systems access sensitive user data.
This benchmark highlights a critical, overlooked aspect of AI privacy—interdependent privacy—which will be crucial for the ethical deployment and public trust of AI agents handling sensitive or shared information.
The focus on AI privacy shifts from individual-centric risks to include interdependent privacy, requiring new evaluation methods and design principles for LLMs operating in complex social contexts.
- · AI ethics researchers
- · Privacy-preserving AI developers
- · Regulatory bodies
- · Users of personal AI assistants
- · LLM developers ignoring IDP
- · Black-box AI models
- · Centralized data platforms
IDP-Bench will become a standard for evaluating privacy capabilities of LLMs, influencing model design and deployment guidelines.
Increased focus on 'contextual integrity' in AI will drive the development of new privacy protection techniques and data governance frameworks.
Public distrust in AI due to privacy breaches could be mitigated, accelerating broader societal adoption of AI systems that transparently manage interdependent data.
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Read at arXiv cs.AI