CounterPersona: Append-Only Defense Against Unauthorized Persona Skill Distillation
CounterPersona appends targeted counter-persona evidence after data collection to block AI systems from distilling an individual's behavioral patterns into reusable skills.
CounterPersona defends against unauthorized persona skill distillation, where attackers extract recurring patterns from collected personal data to replicate an individual's behavior. Unlike perturbation-based defenses that require modifying data before collection, it works in an append-only setting where historical records cannot be altered or revoked. It constructs targeted counter-persona evidence, packs compatible behavioral states into compact realization units, and strengthens them via rationale-guided consistency rewriting. Experiments show strong effectiveness across lexical, semantic, and LLM-based measures, remaining robust across different distillers.
- Defends against persona skill distillation when collected data cannot be modified or revoked
- Append-only approach avoids perturbation defenses requiring pre-collection data changes
- Combines counter-persona evidence, compact realization units, rationale-guided rewriting
- Effective across lexical, semantic, and LLM measures; robust across distillers
Full article147 words · extracted from arxiv.org · click to collapse
Persona skill distillation can extract recurring patterns from personal information and encode them into reusable skills, enabling AI systems to closely replicate an individual's behavior. However, such replication also raises serious concerns regarding personal privacy and labor autonomy. Unlike existing perturbation-based defenses that require individuals to modify their data before collection, once historical records are collected by an attacker, they can no longer be altered, sanitized, or revoked. Therefore, such defenses are difficult to adapt to this append-only setting. To solve this challenge, we introduce CounterPersona, which constructs targeted counter-persona evidence, packs compatible behavioral states into compact realization units, and strengthens them through rationale-guided consistency rewriting. We conduct extensive experiments showing that CounterPersona achieves strong and consistent effectiveness across lexical, semantic, and LLM-based measures, while remaining robust across distillers. Our work establishes a skill anti-distillation paradigm for protecting personal privacy and labor autonomy against unauthorized skill distillation.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.15097