Abstract
The collection of multidimensional crowdsourced data has caused a public concern because of the privacy issues. To address it, local differential privacy (LDP) is proposed to protect the crowdsourced data without much loss of usage, which is popularly used in practice. However, the existing LDP protocols ignore users’ personal privacy requirements in spite of offering good utility for multidimensional crowdsourced data. In this paper, we consider the personality of data owners in protection and utilization of their multidimensional data by introducing the notion of personalized LDP (PLDP). Specifically, we design personalized multiple optimized unary encoding (PMOUE) to perturb data owners’ data, which satisfies ϵ total -PLDP. Then, the aggregation algorithm for frequency estimation on multidimensional data under PLDP is developed, which is described in two situations. Experiments are conducted on four real datasets, and the results show that the proposed aggregation algorithm yields high utility. Moreover, case studies with four real datasets demonstrate the efficiency and superiority of the proposed scheme.
Highlights
Companies and institutions have noticed the big value of the data and are highly motivated to collect highdimensional crowdsourced data to make data-driven decisions. e collection and analysis of data are beneficial to companies as well as the society; the data owners’ privacy makes the biggest concern
An aggregation algorithm is developed for joint distribution estimation on multidimensional data under PLDP. e contributions can be summarized as follows: (1) We propose a new privacy notion called personalized local differential privacy (PLDP), which allows personalized privacy protection for different inputs than LDP
Our scheme allowed the data owner to only share a part of his data. us, the joint distribution of the high-dimensional data can only be obtained by synthesizing that of the low-dimensional data
Summary
Companies and institutions have noticed the big value of the data and are highly motivated to collect highdimensional crowdsourced data to make data-driven decisions. e collection and analysis of data are beneficial to companies as well as the society; the data owners’ privacy makes the biggest concern. E collection and analysis of data are beneficial to companies as well as the society; the data owners’ privacy makes the biggest concern. Local differential privacy (LDP) [1, 2] has been found practical value in collection and utilization of data owners’ data with the privacy preserved. In an LDP scheme, the data owners perturb their sensitive data before data outsourcing and report the perturbed data to the server. In this way, the server cannot infer the owners’ actual data with strong confidence, can still make the accurate estimation of data distribution as it was inferred from the unperturbed data. Microsoft designs the LDP scheme to collect application telemetry to improve user experience [4]
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