MaskClaw: Edge-Side Personalized Privacy Arbitration for GUI Agents with Behavior-Driven Skill Evolution

• URL: http://arxiv.org/abs/2605.28646v2
• Date: Fri, 29 May 2026 04:03:46 GMT
• Title: MaskClaw: Edge-Side Personalized Privacy Arbitration for GUI Agents with Behavior-Driven Skill Evolution
• Abstract summary: MaskClaw is an edge-side privacy arbitrator for GUI agents.<n>It extracts local visual evidence, retrieves user- and task-specific policy memory, and decides Allow, Mask, or Ask.<n>In five designed skill-evolution scenarios, it turns corrections, cancellations, and edits into reusable privacy skills.
• Score: 16.45561196198935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: GUI agents rely on screenshots to infer intent and operate across applications, but these screenshots often contain private messages, medical records, payment credentials, and workplace-specific workflows. Privacy decisions in this setting depend on task, recipient, application state, and user role, yet static PII detectors miss these boundaries and cloud-side VLM reasoning can upload the raw screen before deciding what should be protected. We present MaskClaw, an edge-side privacy arbitrator for GUI agents. MaskClaw extracts local visual evidence, retrieves user- and task-specific policy memory, and decides Allow, Mask, or Ask before raw screenshots leave a trusted user- or organization-controlled environment. In five designed skill-evolution scenarios, it turns corrections, cancellations, and edits into reusable privacy skills checked by a sandbox gate. We introduce P-GUI-Evo, a benchmark built from real UI patterns, reconstructed HTML screens, and sanitized labels. Experiments show that pattern matching, cloud reasoning, and routing alone tend to over-confirm, over-mask, or expose raw screenshots under the same protocol. The artifact is available at https://github.com/Theodora-Y/MaskClaw.

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