Set-valued data collection with local differential privacy based on category hierarchy

Abstract

<abstract> <p>Set-valued data is extremely important and widely used in sensor technology and application. Recently, privacy protection for set-valued data under differential privacy (DP) has become a research hotspot. However, the DP model assumes that the data center is trustworthy, consequently, increasingly attention has been paid to the application of the local differential privacy model (LDP) for set-valued data. Constrained by the local differential privacy model, most methods randomly respond to the subset of set-valued data, and the data collector conducts statistics on the received data. There are two main problems with this kind of method: one is that the utility function used in the random response loses too much information; the other is that the privacy protection of the set-valued data category is usually ignored. To solve these problems, this paper proposes a set-valued data collection method (SetLDP) based on the category hierarchy under the local differential privacy model. The core idea is to first make a random response to the existence of the category, continue to disturb the item count if the category exists, and finally randomly respond to a candidate itemset based on the new utility function. Theory analysis and experimental results show that the SetLDP can not only preserve more information, but also protect the category private information in set-valued data.</p> </abstract>

Support Personalized Weighted Local Differential Privacy Skyline Query

“…Sun et al [23] applied random response techniques to the frequent itemset mining for personalized privacy requirements. Ouyang et al [24] introduced a set-valued data collection approach (SetLDP) based on a category hierarchy under a local differential privacy model, whose central concept is to first to provide a random response to the presence of a category, and the results show that it can well protect the privacy information in the set-valued data. Lan et al [25] proposed personalized differential privacy (iDP-SC) based on a spectral clustering algorithm to reduce the local sensitivity by the introduction of the spectral clustering algorithm, and the noise reduction generated by spectral clustering compensates for the information distortion error introduced by itself.…”