Abstract
In many cases, competing parties who have private data may collaboratively conduct privacy-preserving distributed data analysis (PPDA) tasks to learn beneficial data models or analysis results. Most often, the competing parties have different incentives. Although certain PPDA techniques guarantee that nothing other than the final analysis result is revealed, it is impossible to verify whether participating parties are truthful about their private input data. Unless proper incentives are set, current PPDA techniques cannot prevent participating parties from modifying their private inputs. This raises the question of how to design incentive compatible privacy-preserving data analysis techniques that motivate participating parties to provide truthful inputs. In this paper, we first develop key theorems, then based on these theorems, we analyze certain important privacy-preserving data analysis tasks that could be conducted in a way that telling the truth is the best choice for any participating party.©2013 IEEE.
Recommended Citation
M. Kantarcioglu and W. Jiang, "Incentive Compatible Privacy-preserving Data Analysis," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 6, pp. 1323 - 1335, article no. 6171190, Institute of Electrical and Electronics Engineers, Jun 2013.
The definitive version is available at https://doi.org/10.1109/TKDE.2012.61
Department(s)
Computer Science
Keywords and Phrases
Noncooperative computation; Privacy; Secure multiparty computation
International Standard Serial Number (ISSN)
1041-4347
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jun 2013
