PGPregel: An End-to-End System for Privacy-Preserving Graph Processing in Geo-Distributed Data Centers

Amelie Chi Zhou, Ruibo Qiu, Thomas Lambert, Tristan Allard, Shadi Ibrahim, Amr El Abbadi

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationSoCC '22: Proceedings of the 13th Symposium on Cloud Computing
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages17
ISBN (Electronic)9781450394147
Publication statusPublished - 7 Nov 2022
Event13th Annual ACM Symposium on Cloud Computing, SoCC 2022 - San Francisco, United States
Duration: 7 Nov 202211 Nov 2022 (Conference Proceeding)


Conference13th Annual ACM Symposium on Cloud Computing, SoCC 2022
Country/TerritoryUnited States
CitySan Francisco
Internet address

Scopus Subject Areas

  • Artificial Intelligence
  • Information Systems
  • Software
  • Computational Theory and Mathematics
  • Computer Science Applications


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