So sánh phương pháp

	Quyền riêng tư vi phân	k-Anonymity: Bảo vệ Quyền riêng tư Cá nhân trong Dữ liệu Được Công bố
Lĩnh vực	Quyền riêng tư	Quyền riêng tư
Họ	Machine learning	Machine learning
Năm ra đời	2006	2002
Người khởi xướng	Cynthia Dwork	Latanya Sweeney
Loại	Privacy-preserving randomized mechanism	Privacy-preserving data transformation
Công trình gốc	Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12.	Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557–570.
Tên gọi khác	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik	k-Anonymization, k-Anonymous Microdata, Quasi-Identifier Suppression Model, k-Anonimlik
Liên quan	3	2
Tóm tắt	Differential privacy is a mathematical framework for releasing statistical information about a dataset while providing rigorous guarantees that individual records cannot be identified or inferred. Introduced by Cynthia Dwork in 2006, it formalizes privacy as a probabilistic bound: any single individual's presence or absence in the dataset changes the output distribution by at most a multiplicative factor of e^ε, where ε is the privacy budget controlling the privacy–utility tradeoff.	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.