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- Local differential privacy (LDP) is a model of differential privacy with the added restriction that even if an adversary has access to the personal responses of an individual in the database, that adversary will still be unable to learn too much about 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. With society growing ever more reliant on the digital world and data driven decision making, the smart devices we all have collect extensive statistics and analysis of our personal data that threatens the privacy of users. The driven data fusion and analysis techniques only exposes the users to become more vulnerable to attacks and disclosure in the big data era. To aid in this privacy concern, local differential privacy is one possible solution. Local differential privacy (LDP) is seen as a widely recognized and prevalent privacy model with distributed architecture which can provide strong privacy guarantees for each user while collecting and analyzing data from privacy leaks on both the client and server side. Furthermore, pendant of any assumptions on the third-party servers, LDP has been imposed as the cutting-edge of research on privacy protection and risen in prominence not only from theoretical interests, but also subsequently from a practical perspective. Due to its powerfulness, LDP has been widely adopted to alleviate the privacy concerns of each user. (en)
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- Local differential privacy (LDP) is a model of differential privacy with the added restriction that even if an adversary has access to the personal responses of an individual in the database, that adversary will still be unable to learn too much about 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. (en)
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- Local differential privacy (en)
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