Privacy Preserving PageRank Algorithm By Using Secure Multi-Party Computation
This work addresses privacy concerns for users in distributed graph analysis, but it is incremental as it applies known encryption techniques to a specific algorithm.
The paper tackles the problem of computing PageRank while preserving privacy by using secure multi-party computation with homomorphic encryption based on the Paillier scheme, enabling users to encrypt their graph data and have parties compute encrypted results without learning others' data.
In this work, we study the problem of privacy preserving computation on PageRank algorithm. The idea is to enforce the secure multi party computation of the algorithm iteratively using homomorphic encryption based on Paillier scheme. In the proposed PageRank computation, a user encrypt its own graph data using asymmetric encryption method, sends the data set into different parties in a privacy-preserving manner. Each party computes its own encrypted entity, but learns nothing about the data at other parties.