PrivAG: Analyzing Attributed Graph Data with Local Differential Privacy

Attributed graph data is powerful to describe relational information in various areas, such as social links through numerous web services and citation/reference relations in the collaboration network. Taking advantage of attributed graph data, service providers can model complex systems and capture diversified interactions to achieve better business performance. However, privacy concern is a huge obstacle to collect and analyze user's attributed graph data. Existing studies on protecting private graph data mainly focus on edge local differential privacy(LDP), which might be insufficient in some highly sensitive scenarios. In this paper, we present a novel privacy notion that is stronger than edge LDP, and investigate approaches to analyze attributed graphs under this notion. To neutralize the effect of excessively introduced noise, we propose PrivAG, a privacy-preserving framework that protects attributed graph data in the local setting while providing representative graph statistics. The effectiveness and efficiency of PrivAG framework is validated through extensive experiments.