arXiv:2607.02932v1 Announce Type: cross Abstract: Privacy is an important challenge when users interact with AI chatbots, since users may share sensitive information, explicitly or implicitly, and AI chatbots can use this information for user profiling. In this paper, we aim to protect user privacy via a user-side mechanism that transforms sensitive information in a user prompt, while preserving enough information to elicit a useful response from the chatbot. This approach faces an inherent tradeoff between protecting privacy (i.e., avoiding profiling) and preserving utility (i.e., getting per
The proliferation of AI chatbots and increasing user interaction with them necessitates immediate solutions for privacy concerns and data leakage, driving research into user-side protection mechanisms.
This development addresses a critical user adoption barrier for AI by offering a technical solution to protect sensitive information, enabling broader and more confident engagement with AI platforms.
The ability to obfuscate sensitive data at the user prompt level changes the privacy landscape for AI interactions, shifting some control back to the user without sacrificing utility for the AI's response.
- · AI chatbot users
- · Developers of privacy-preserving AI tools
- · AI companies focused on enterprise and regulated sectors
- · Ad-tech companies reliant on unconstrained user data from AI interactions
- · AI models that rely on direct, unfiltered sensitive user data for profiling
Increased user trust and adoption of AI chatbots due to enhanced privacy safeguards.
New standards and regulations emerging for prompt privacy and data obfuscation in AI interactions.
A shift in business models for AI services, potentially moving away from data monetization towards subscription or utility-based services based on privacy-enhanced interactions.
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Read at arXiv cs.AI