Semi-Private Computation of Data Similarity with Applications to Data Valuation and Pricing

René Bødker Christensen*, Shashi Raj Pandey, Petar Popovski

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


Consider two data providers that want to contribute data to a certain learning model. Recent works have shown that the value of the data of one of the providers is dependent on the similarity with the data owned by the other provider. It would thus be beneficial if the two providers can calculate the similarity of their data, while keeping the actual data private. In this work, we devise multiparty computation-protocols to compute similarity of two data sets based on correlation, while offering controllable privacy guarantees. We consider a simple model with two participating providers and develop methods to compute exact and approximate correlation, respectively, with controlled information leakage. Both protocols have computational and communication complexities that are linear in the number of data samples. We also provide general bounds on the maximal error in the approximation case, and analyse the resulting errors for practical parameter choices.
Original languageEnglish
JournalI E E E Transactions on Information Forensics and Security
Pages (from-to)1978-1988
Number of pages11
Publication statusPublished - 5 Apr 2023


  • data similarity
  • information leakage
  • multiparty computation
  • sample correlation
  • secure protocols


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