Privacy and Security in Distributed Data Markets

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Data markets play a pivotal role in modern industries by facilitating the exchange of data for predictive modeling, targeted marketing, and research. However, as data becomes a valuable commodity, privacy and security concerns have grown, particularly regarding the personal information of individuals. This tutorial explores privacy and security issues when integrating different data sources in data market platforms. As motivation for the importance of enforcing privacy requirements, we discuss attacks on data markets focusing on membership inference and reconstruction attacks. We also discuss security vulnerabilities in decentralized data marketplaces, including adversarial manipulations by buyers or sellers. We provide an overview of privacy and security mechanisms designed to mitigate these risks. In order to enforce the least amount of trust for buyers and sellers, we focus on distributed protocols. Finally, we conclude with opportunities for future research on understanding and mitigating privacy and security concerns in distributed data markets.

Original languageEnglish (US)
Title of host publicationSIGMOD-Companion 2025 - Companion of the 2025 International Conference on Management of Data
EditorsAmol Deshpande, Ashraf Aboulnaga, Babak Salimi, Badrish Chandramouli, Bill Howe, Boon Thau Loo, Boris Glavic, Carlo Curino, Daisy Zhe Wang, Dan Suciu, Daniel Abadi, Divesh Srivastava, Eugene Wu, Faisal Nawab, Ihab Ilyas, Jeffrey Naughton, Jennie Rogers, Jignesh Patel, Joy Arulraj, Jun Yang, Karima Echihabi, Kenneth Ross, Khuzaima Daudjee, Laks Lakshmanan, Minos Garofalakis, Mirek Riedewald, Mohamed Mokbel, Mourad Ouzzani, Oliver Kennedy, Oliver Kennedy, Paolo Papotti, Peter Alvaro, Peter Bailis, Renee Miller, Senjuti Basu Roy, Sergey Melnik, Stratos Idreos, Sudeepa Roy, Theodoros Rekatsinas, Viktor Leis, Wenchao Zhou, Wolfgang Gatterbauer, Zack Ives
PublisherAssociation for Computing Machinery
Pages775-787
Number of pages13
ISBN (Electronic)9798400715648
DOIs
StatePublished - Jun 22 2025
Event2025 ACM SIGMOD/PODS International Conference on Management of Data, SIGMOD-Companion 2025 - Berlin, Germany
Duration: Jun 22 2025Jun 27 2025

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Conference

Conference2025 ACM SIGMOD/PODS International Conference on Management of Data, SIGMOD-Companion 2025
Country/TerritoryGermany
CityBerlin
Period6/22/256/27/25

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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