Local differential privacy (LDP) is a model of differential privacy with the added requirement that if an adversary has access to the personal responses of an individual in the database, that adversary will still be unable to learn much of the user's personal data. This is contrasted with global differential privacy, a model of differential privacy that incorporates a central aggregator with access to the raw data.[1]
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Local differential privacy (LDP) is an approach to mitigate the concern of data fusion and analysis techniques used to expose individuals to attacks and disclosures. LDP is a well-known privacy model for distributed architectures that aims to provide privacy guarantees for each user while collecting and analyzing data, protecting from privacy leaks for the client and server.[2] LDP has been widely adopted to alleviate contemporary privacy concerns in the era of big data.[3]
^"Local vs. global differential privacy – Ted is writing things". desfontain.es. Retrieved 2020-02-10.
^Joseph, Matthew; Roth, Aaron; Ullman, Jonathan; Waggoner, Bo (2018-11-19). "Local Differential Privacy for Evolving Data". arXiv:1802.07128 [cs.LG].
^Wang, Teng; Zhang, Xuefeng; Feng, Jingyu; Yang, Xinyu (2020-12-08). "A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis". Sensors (Basel, Switzerland). 20 (24): 7030. arXiv:2010.05253. Bibcode:2020Senso..20.7030W. doi:10.3390/s20247030. ISSN 1424-8220. PMC 7763193. PMID 33302517.
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