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WIP: PODS: Privacy Compliant Scalable Decentralized Data Services
RISE Research Institutes of Sweden, Digital Systems, Data Science. KTH Royal Institute of Technology, Sweden.ORCID iD: 0000-0002-7119-5234
RISE Research Institutes of Sweden, Digital Systems, Data Science. KTH Royal Institute of Technology, Sweden.ORCID iD: 0000-0002-9351-8508
KTH Royal Institute of Technology, Sweden.ORCID iD: 0000-0002-2659-5271
2021 (English)In: Heterogeneous Data Management, Polystores, and Analytics for Healthcare: VLDB Workshops, Poly 2021 and DMAH 2021, Virtual Event, August 20, 2021, Revised Selected Papers / [ed] El Kindi Rezig, Vijay Gadepally, Timothy Mattson, Michael Stonebraker, Tim Kraska, Fusheng Wang, Gang Luo, Jun Kong, Alevtina Dubovitskaya, Springer , 2021, p. 70-82Conference paper, Published paper (Refereed)
Abstract [en]

Modern data services need to meet application developers’ demands in terms of scalability and resilience, and also support privacy regulations such as the EU’s GDPR. We outline the main systems challenges of supporting data privacy regulations in the context of large-scale data services, and advocate for causal snapshot consistency to ensure application-level and privacy-level consistency. We present Pods, an extension to the dataflow model that allows external services to access snapshotted operator state directly, with built-in support for addressing the outlined privacy challenges, and summarize open questions and research directions.

Place, publisher, year, edition, pages
Springer , 2021. p. 70-82
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 12921
Keywords [en]
Dataflow model, Decentralized data services, GDPR, Privacy compliance
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-64919DOI: 10.1007/978-3-030-93663-1_7Scopus ID: 2-s2.0-85122590984OAI: oai:DiVA.org:ri-64919DiVA, id: diva2:1762466
Conference
VLDB workshops: International Workshop on Polystore Systems for Heterogeneous Data in Multiple Databases with Privacy and Security Assurances, Poly 2021 and 7th International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2021, Virtual, Online, 20 August 2021 through 20 August 2021
Note

QC 20220616

Part of proceedings: ISBN 978-3-030-93662-4; 978-3-030-93663-1

Available from: 2023-06-03 Created: 2023-06-03 Last updated: 2023-06-05Bibliographically approved

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Spenger, JonasCarbone, ParisHaller, Philipp

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