Graph processing is a popular computing model for big data analytics. Emerging big data applications are often maintained in multiple geographically distributed (geo-distributed) data centers (DCs) to provide low-latency services to global users. Graph processing in geo-distributed DCs suffers from costly inter-DC data communications. Furthermore, due to increasing privacy concerns, geo-distribution imposes diverse, strict, and often asymmetric privacy regulations that constrain geo-distributed graph processing. Existing graph processing systems fail to address these two challenges. In this paper, we design and implement PGPregel, which is an end-to-end system that provides privacy-preserving graph processing in geo-distributed DCs with low latency and high utility. To ensure privacy, PGPregel smartly integrates Differential Privacy into graph processing systems with the help of two core techniques, namely sampling and combiners, to reduce the amount of inter-DC data transfer while preserving good accuracy of graph processing results. We implement our design in Giraph and evaluate it in real cloud DCs. Results show that PGPregel can preserve the privacy of graph data with low overhead and good accuracy.
Amelie Chi Zhou is a tenured Associate Professor of Shenzhen University, China. She obtained her Ph.D. in Computer Science from Nanyang Technological University, Singapore, in 2016 and worked as a postdoc fellow at Inria Rennes research center from 2016-2017. Her research interests lie in high performance computing, cloud computing, and file and storage systems. Her work has been published in prestigious conferences and journals in parallel and distributed computing field, including SC, HPDC, SoCC, ICDCS, ICPP, ICDE and SIGMOD. She has served as a PC member for a number of top-tiered conferences in the field such as USENIX FAST, ACM/IEEE SC, ACM HPDC, IEEE ICDCS, IEEE ICPP, IEEE Cluster, ACM/IEEE CCGrid and ACM CIKM. She is an Editor of the FGCS journal and an Associate Editor of IEEE Transactions on Parallel and Distributed Computing. She received the IEEE-CS TCHPC Early Career Researchers Award for Excellence in High Performance Computing and the ACM SIGHPC China Rising Star in 2021.
