Private rank aggregation under local differential privacy
نویسندگان
چکیده
منابع مشابه
Gradually Releasing Private Data under Differential Privacy
Aggregating individuals’ data and computing statistics over a population are key ingredients to enable the Internet of Things [1]. Constructing traffic maps from individuals’ GPS traces [2] and performing demand response in smart grids [3], [4] are two examples that involve such data aggregation. Using these statistics, individuals can perform their activities more efficiently; they may choose ...
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Given a collection of rankings of a set of items, rank aggregation seeks to compute a ranking that can serve as a single best representative of the collection. Rank aggregation is a well-studied problem and a number of effective algorithmic solutions have been proposed in the literature. However, when individuals are asked to contribute a ranking, they may be concerned that their personal prefe...
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Many analysis and machine learning tasks require the availability of marginal statistics on multidimensional datasets while providing strong privacy guarantees for the data subjects. Applications for these statistics range from finding correlations in the data to fitting sophisticated prediction models. In this paper, we provide a set of algorithms for materializing marginal statistics under th...
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Differential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result, such that it is provably hard for the adversary to infer the presence or absence of any individual record from the published noisy results. The main objective in differentially private query processing is to maximize the...
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Differential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result, such that it is provably hard for the adversary to infer the presence or absence of any individual record from the published noisy results. The main objective in differentially private query processing is to maximize the...
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ژورنال
عنوان ژورنال: International Journal of Intelligent Systems
سال: 2020
ISSN: 0884-8173,1098-111X
DOI: 10.1002/int.22261