PACS: Privacy-Preserving Attribute-Driven Community Search over Attributed Graphs
Fangyuan Sun
Network and Distributed System Security (NDSS) Symposium 2026 · Day 3 · Privacy & Measurement
PACS enables **privacy-preserving community search** on attributed graphs outsourced to cloud servers. The system allows users to find structurally cohesive communities with the highest attribute relevance scores without revealing the graph data, community attributes, or user queries to the cloud. Built on **homomorphic encryption** and **secure index structures**, PACS achieves **CQA2 adaptive security** (secure against adaptive chosen-query attacks) while completing searches in **milliseconds**. Applications span social network marketing, citation network analysis, and biological network function module identification.
AI review
A cryptographic construction for privacy-preserving graph community search that is technically sound but has no security research relevance. No vulnerabilities, no attacks, no defenses against any threat -- just applied cryptography for a graph analytics use case. The honest-but-curious threat model is weak, and the presentation was difficult to follow.