Linganisha mbinu

	Ushirikiano Salama wa Vyama Nyingi	k-Uthibitisho: Kulinda Faragha ya Mtu Binafsi katika Data Iliyotolewa
Nyanja	Faragha	Faragha
Familia	Machine learning	Machine learning
Mwaka wa asili	1982	2002
Mwanzilishi	Andrew Yao	Latanya Sweeney
Aina	Cryptographic protocol family	Privacy-preserving data transformation
Chanzo asilia	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.
Majina mbadala	MPC, Multi-Party Computation, Privacy-Preserving Computation, Güvenli Çok Taraflı Hesaplama	k-Anonymization, k-Anonymous Microdata, Quasi-Identifier Suppression Model, k-Anonimlik
Zinazohusiana	3	2
Muhtasari	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.