PrivKV: Key-Value Data Collection with Local Differential Privacy

Abstract

Local differential privacy (LDP), where each user perturbs her data locally before sending to an untrusted data collector, is a new and promising technique for privacy-preserving distributed data collection. The advantage of LDP is to enable the collector to obtain accurate statistical estimation on sensitive user data (e.g., location and app usage) without accessing them. However, existing work on LDP is limited to simple data types, such as categorical, numerical, and set-valued data. To the best of our knowledge, there is no existing LDP work on key-value data, which is an extremely popular NoSQL data model and the generalized form of set-valued and numerical data. In this paper, we study this problem of frequency and mean estimation on key-value data by first designing a baseline approach P rivKV within the same "perturbation-calibration" paradigm as existing LDP techniques. To address the poor estimation accuracy due to the clueless perturbation of users, we then propose two iterative solutions P rivKV M and P rivKV M + that can gradually improve the estimation results through a series of iterations. An optimization strategy is also presented to reduce network latency and increase estimation accuracy by introducing virtual iterations in the collector side without user involvement. We verify the correctness and effectiveness of these solutions through theoretical analysis and extensive experimental results.

Locally Differentially Private Frequency Estimation with Consistency

“…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].…”

“…[28] and [22] consider the hierarchy structure and apply the technique of [16]. [37] considers mean estimation and propose to project the result into [0, 1].…”

Privacy-Preserving Cost Minimization in Mobile Crowd Sensing Supported by Edge Computing

“…Different technologies have been developed to protect data privacy in MCS, e.g., based on the theory of distributed agent [17], that of differential privacy [18]- [21] and etc. In the most related work [22], a data privacy protection algorithm for the key-value type is designed. It privatizes the user-executed task (key) and the user's data (value) for the task based on LDP theory, which makes sure that the perturbed data has the same statistical characteristics of the original data, e.g., the same estimated key value and mean value.…”

“…We also implement PrivKV [22] for comparison. PrivKV privatizes the user-executed task (key) and the user's data (value) for the task based on the LDP theory, Which makes sure that the perturbed data has the same statistical characteristics of the original data.…”

Mobile User Location Inference Attacks Fusing with Multiple Background Knowledge in Location-Based Social Networks

“…In the early stage, to protect location privacy, Gruteser and Grunwald [8] proposed a location k-anonymity model which was developed from the well-known k-anonymity concept, that is, the location is made indistinguishable by using the location information of at least k − 1 other users. Then, a great of research efforts [9][10][11][12][13] have been devoted in investigating location privacy protection. The existing location privacy protection mechanisms can be classified into a priori protection and a posterior screening [14].…”