Vertaile menetelmiä

	Turvattu monen osapuolen laskenta	k-Anonymiteetti: Yksilöiden yksityisyyden suojaaminen julkaistussa datassa
Tieteenala	Yksityisyydensuoja	Yksityisyydensuoja
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	1982	2002
Kehittäjä	Andrew Yao	Latanya Sweeney
Tyyppi	Cryptographic protocol family	Privacy-preserving data transformation
Alkuperäislähde	Yao, A. C. (1982). Protocols for secure computations. 23rd Annual Symposium on Foundations of Computer Science, 160–164.	Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557–570.
Rinnakkaisnimet	MPC, Multi-Party Computation, Privacy-Preserving Computation, Güvenli Çok Taraflı Hesaplama	k-Anonymization, k-Anonymous Microdata, Quasi-Identifier Suppression Model, k-Anonimlik
Liittyvät	3	2
Tiivistelmä	Secure Multi-Party Computation (SMPC) is a cryptographic paradigm that enables two or more parties to jointly compute a function over their private inputs without revealing those inputs to one another. Introduced by Andrew Yao in 1982 through his seminal garbled-circuit construction, SMPC provides provable privacy guarantees grounded in computational hardness assumptions. It underpins modern privacy-preserving data analysis, enabling collaborative computation on sensitive datasets in finance, healthcare, and machine learning.	k-Anonymity is a formal privacy model introduced by Latanya Sweeney in 2002 to protect individuals when personal data is released for research or public use. It requires that every record in a published dataset be indistinguishable from at least k−1 other records with respect to a designated set of quasi-identifying attributes — such as age, gender, and ZIP code — preventing re-identification by linking released data to external sources.