PGPregel: An End-to-End System for Privacy-Preserving Graph Processing in Geo-Distributed Data Centers - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2022

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

Ruibo Qiu
  • Function : Author
  • PersonId : 1196815
Tristan Allard
Amr El Abbadi
  • Function : Author
  • PersonId : 942093

Abstract

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.
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Dates and versions

hal-03879423 , version 1 (01-12-2022)

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Amelie Chi Zhou, Ruibo Qiu, Thomas Lambert, Tristan Allard, Shadi Ibrahim, et al.. PGPregel: An End-to-End System for Privacy-Preserving Graph Processing in Geo-Distributed Data Centers. SoCC '22: ACM Symposium on Cloud Computing, Association for Computing Machinery, Nov 2022, San Francisco California, United States. pp.386-402, ⟨10.1145/3542929.3563474⟩. ⟨hal-03879423⟩
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