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

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Názov: PGPregel: An End-to-End System for Privacy-Preserving Graph Processing in Geo-Distributed Data Centers
Autori: Zhou, Amelie Chi, Qiu, Ruibo, Lambert, Thomas, Allard, Tristan, Ibrahim, Shadi, El Abbadi, Amr
Prispievatelia: Shenzhen University Shenzhen = 深圳大学 (SZU), Web Scale Trustworthy Collaborative Service Systems (COAST), Centre Inria de l'Université de Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Networks, Systems and Services (LORIA - NSS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Security & PrIvaCY (SPICY), SYSTÈMES LARGE ÉCHELLE (IRISA-D1), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT), Design and Implementation of Autonomous Distributed Systems (MYRIADS), Centre Inria de l'Université de Rennes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SYSTÈMES LARGE ÉCHELLE (IRISA-D1), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), University of California Santa Barbara (UC Santa Barbara), University of California (UC), In addition to the ANR projects cited above, this work is supported by the National Natural Science Foundation of China 62172282, Guangdong Natural Science Foundation (2022A1515010122, 2019A1515012053), Guangdong Provincial Key Laboratory of Popular High Performance Computers, Shenzhen Science and Technology Foundation JCYJ20210324093212034, the Tencent “Rhinoceros Birds” - Scientific Research Foundation for Young Teachers of Shenzhen University., Association for Computing Machinery, Ada Gavrilovska, Deniz Altınbüken, Carsten Binnig, ANR-16-CE25-0014,KerStream,Traitement de données massives: allons au-delà d'Hadoop!(2016), ANR-16-CE23-0004,CROWDGUARD,Confidentialité et efficacité dans les plates-formes de crowdsourcing(2016)
Zdroj: Proceedings of the 13th Symposium on Cloud Computing ; SoCC '22: ACM Symposium on Cloud Computing ; https://hal.science/hal-03879423 ; 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⟩
Informácie o vydavateľovi: CCSD
ACM
Rok vydania: 2022
Predmety: Graph processing, Differential privacy, Cloud Computting, GDPR - General Data Protection Regulation, Privacy-preserving data analytics, Social network Analysis SNA, [INFO]Computer Science [cs]
Geografické téma: San Francisco California, United States
Popis: International audience ; 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.
Druh dokumentu: conference object
Jazyk: English
ISBN: 978-1-4503-9414-7
1-4503-9414-0
DOI: 10.1145/3542929.3563474
Dostupnosť: https://hal.science/hal-03879423
https://hal.science/hal-03879423v1/document
https://hal.science/hal-03879423v1/file/main.pdf
https://doi.org/10.1145/3542929.3563474
Rights: http://creativecommons.org/licenses/by-nc/ ; info:eu-repo/semantics/OpenAccess
Prístupové číslo: edsbas.6CD5E9C6
Databáza: BASE
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  Data: PGPregel: An End-to-End System for Privacy-Preserving Graph Processing in Geo-Distributed Data Centers
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  Data: Shenzhen University Shenzhen = 深圳大学 (SZU)<br />Web Scale Trustworthy Collaborative Service Systems (COAST)<br />Centre Inria de l'Université de Lorraine<br />Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Networks, Systems and Services (LORIA - NSS)<br />Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA)<br />Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA)<br />Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)<br />Security & PrIvaCY (SPICY)<br />SYSTÈMES LARGE ÉCHELLE (IRISA-D1)<br />Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA)<br />Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes)<br />Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique)<br />Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes)<br />Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA)<br />Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)<br />Design and Implementation of Autonomous Distributed Systems (MYRIADS)<br />Centre Inria de l'Université de Rennes<br />Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SYSTÈMES LARGE ÉCHELLE (IRISA-D1)<br />Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique)<br />University of California Santa Barbara (UC Santa Barbara)<br />University of California (UC)<br />In addition to the ANR projects cited above, this work is supported by the National Natural Science Foundation of China 62172282, Guangdong Natural Science Foundation (2022A1515010122, 2019A1515012053), Guangdong Provincial Key Laboratory of Popular High Performance Computers, Shenzhen Science and Technology Foundation JCYJ20210324093212034, the Tencent “Rhinoceros Birds” - Scientific Research Foundation for Young Teachers of Shenzhen University.<br />Association for Computing Machinery<br />Ada Gavrilovska<br />Deniz Altınbüken<br />Carsten Binnig<br />ANR-16-CE25-0014,KerStream,Traitement de données massives: allons au-delà d'Hadoop!(2016)<br />ANR-16-CE23-0004,CROWDGUARD,Confidentialité et efficacité dans les plates-formes de crowdsourcing(2016)
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  Data: Proceedings of the 13th Symposium on Cloud Computing ; SoCC '22: ACM Symposium on Cloud Computing ; https://hal.science/hal-03879423 ; 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⟩
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  Data: International audience ; 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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