TY - JOUR
T1 - Semi-Private Computation of Data Similarity with Applications to Data Valuation and Pricing
AU - Christensen, René Bødker
AU - Pandey, Shashi Raj
AU - Popovski, Petar
PY - 2023/4/5
Y1 - 2023/4/5
N2 - 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.
AB - 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.
KW - data similarity
KW - information leakage
KW - multiparty computation
KW - sample correlation
KW - secure protocols
UR - http://www.scopus.com/inward/record.url?scp=85151504650&partnerID=8YFLogxK
U2 - 10.1109/TIFS.2023.3259879
DO - 10.1109/TIFS.2023.3259879
M3 - Journal article
SN - 1556-6013
VL - 18
SP - 1978
EP - 1988
JO - I E E E Transactions on Information Forensics and Security
JF - I E E E Transactions on Information Forensics and Security
ER -