Estimating Numerical Distributions under Local Differential Privacy

Li, Zitao; Wang, Tianhao; Lopuhaä-Zwakenberg, Milan; Li, Ninghui; Škorić, Boris

doi:10.1145/3318464.3389700

Cited by 59 publications

(61 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[50] borrowed the definition of personalized local differential privacy (PLDP) [51] and adopted the Harmony-mean method for mean estimation over numerical data. Li et al [52] studied the problem of locally differentially private distribution estimation on numerical attribute data. ey firstly proposed to apply the Alternating Direction Method of Multipliers optimization to post-processing Hierarchical Histograms (HH) [38] for estimation improvement, which is called HH-ADMM method and is regarded as Categorical Frequency Oracles to reconstruct the numerical distribution by discretizing numerical domain into the categorical domain.…”

Section: Mean Estimation Under Ldpmentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy

Xiong

Liu

et al. 2020

Security and Communication Networks

View full text Add to dashboard Cite

With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

show abstract

Section: Mean Estimation Under Ldpmentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy

Xiong

Liu

et al. 2020

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…LDP frequency oracle is also a building block for other analytical tasks, e.g., finding heavy hitters [4], [7], [34], frequent itemset mining [26], [33], releasing marginals under LDP [27], [8], [38], key-value pair estimation [37], [15], evolving data monitoring [18], [13], and (multi-dimensional) range analytics [32], [22]. Mean estimation is also a building block in LDP; most of existing work transforms the numerical value to a discrete value using stochastic round, and then apply frequency oracles [11], [29], [24].…”

Section: Related Workmentioning

confidence: 99%

Locally Differentially Private Frequency Estimation with Consistency

Wang

Lopuhaä-Zwakenberg

et al. 2020

Proceedings 2020 Network and Distributed System Security Symposium

Self Cite

187

578

View full text Add to dashboard Cite

Local Differential Privacy (LDP) protects user privacy from the data collector. LDP protocols have been increasingly deployed in the industry. A basic building block is frequency oracle (FO) protocols, which estimate frequencies of values. While several FO protocols have been proposed, the design goal does not lead to optimal results for answering many queries. In this paper, we show that adding post-processing steps to FO protocols by exploiting the knowledge that all individual frequencies should be non-negative and they sum up to one can lead to significantly better accuracy for a wide range of tasks, including frequencies of individual values, frequencies of the most frequent values, and frequencies of subsets of values. We consider 10 different methods that exploit this knowledge differently. We establish theoretical relationships between some of them and conducted extensive experimental evaluations to understand which methods should be used for different query tasks.

show abstract

“…Considering RR only works for binary data, some perturbation mechanisms generalize and optimize it, such as direct encoding (DE) [24], histogram encoding (HE) [25], unary encoding (UE) [8,26], and local hashing (LH) [6,27].…”

Section: Preliminariesmentioning

confidence: 99%

PLDP: Personalized Local Differential Privacy for Multidimensional Data Aggregation

Shen

Xia

2021

Security and Communication Networks

View full text Add to dashboard Cite

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.

show abstract

Estimating Numerical Distributions under Local Differential Privacy

Cited by 59 publications

References 28 publications

A Comprehensive Survey on Local Differential Privacy

A Comprehensive Survey on Local Differential Privacy

Locally Differentially Private Frequency Estimation with Consistency

PLDP: Personalized Local Differential Privacy for Multidimensional Data Aggregation

Contact Info

Product

Resources

About