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AG Audience

English

30 Minutes

With the increasing adoption of vector databases in various machine learning and data-driven applications, ensuring privacy and security while maintaining efficiency has become a critical challenge. A key concern is the exposure of access patterns, which can inadvertently reveal sensitive information about queries and underlying data. In this talk, I will present my work on securing vector databases against access pattern attacks by integrating obfuscation techniques into indexing mechanisms. Specifically, I focus on applying these techniques to hierarchical navigable small-world (HNSW) graphs, a state-of-the-art indexing method. My approach strikes a balance between privacy and performance, offering a scalable solution for practical applications. Through this research, I aim to demonstrate how combining advanced obfuscation strategies with robust indexing methods can mitigate privacy risks without compromising the system's efficiency.

Ina Geisler

+49 681 9325 1802

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