পদ্ধতির তুলনা করুন

	ডিফারেনশিয়াল প্রাইভেসি	ফেডারেটেড লার্নিং	কে-অ্যানোনিমিটি: প্রকাশিত তথ্যে ব্যক্তিগত গোপনীয়তা সুরক্ষা
ক্ষেত্র	গোপনীয়তা	গোপনীয়তা	গোপনীয়তা
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	2006	2017	2002
প্রবর্তক	Cynthia Dwork	McMahan et al.	Latanya Sweeney
ধরন	Privacy-preserving randomized mechanism	Distributed privacy-preserving machine learning	Privacy-preserving data transformation
মৌলিক উৎস	Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12.	McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273–1282.	Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557–570.
অপর নাম	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme	k-Anonymization, k-Anonymous Microdata, Quasi-Identifier Suppression Model, k-Anonimlik
সম্পর্কিত	3	3	2
সারসংক্ষেপ	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.	Federated Learning is a distributed machine learning paradigm introduced by McMahan et al. in 2017 in which a global model is trained collaboratively across multiple decentralized clients — such as mobile devices or hospital systems — without ever transferring raw data to a central server. Each participant computes model updates locally using its private data; only those updates, not the underlying data, are communicated and aggregated by the server to improve the shared model.	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.