在注重隱私的助理中實現情境完整性

Operationalizing Contextual Integrity in Privacy-Conscious Assistants

August 5, 2024

作者: Sahra Ghalebikesabi, Eugene Bagdasaryan, Ren Yi, Itay Yona, Ilia Shumailov, Aneesh Pappu, Chongyang Shi, Laura Weidinger, Robert Stanforth, Leonard Berrada, Pushmeet Kohli, Po-Sen Huang, Borja Balle

cs.AI

摘要

先進的人工智慧助理結合前沿的LLMs和工具訪問權限，以自主方式代表用戶執行複雜任務。儘管這類助理的幫助程度可以隨著訪問用戶信息（包括郵件和文件）而顯著提高，但這也引發了關於助理未經用戶監督與第三方分享不當信息的隱私擔憂。為了引導信息分享助理按照隱私期望行事，我們提出將情境完整性（CI）具體化的方法，該框架將隱私與特定情境中信息的適當流動相提並論。具體來說，我們設計並評估了多種策略，以引導助理的信息分享行為符合CI的要求。我們的評估基於一個由合成數據和人工標註組成的新型表單填寫基準，結果顯示，促使前沿的LLMs進行基於CI的推理會產生良好的效果。

English

Advanced AI assistants combine frontier LLMs and tool access to autonomously perform complex tasks on behalf of users. While the helpfulness of such assistants can increase dramatically with access to user information including emails and documents, this raises privacy concerns about assistants sharing inappropriate information with third parties without user supervision. To steer information-sharing assistants to behave in accordance with privacy expectations, we propose to operationalize contextual integrity (CI), a framework that equates privacy with the appropriate flow of information in a given context. In particular, we design and evaluate a number of strategies to steer assistants' information-sharing actions to be CI compliant. Our evaluation is based on a novel form filling benchmark composed of synthetic data and human annotations, and it reveals that prompting frontier LLMs to perform CI-based reasoning yields strong results.