A New Distributed User-Demand-Driven Location Privacy Protection Scheme for Mobile Communication Network

Abstract

With the development of mobile communication networks and intelligent terminals, recent years have witnessed a rapid popularization of location-based service (LBS). While obtaining convenient services, the exploitation of mass location data is inevitably leading to a serious concern about location privacy security. Obviously, high quality of service (QoS) will result in poor location privacy protection, so that a trade-off is needed to fulfill users’ individual demands for both sides. Although existing methods perform well in certain scenarios, few have considered the abovementioned balance problem. Therefore, by combiningk-anonymity-based cloaking technique and obfuscation method, a new distributed user-demand-driven (DUDD) location privacy protection scheme is put forward in this paper. The basic idea is still to select a subcloaking area within the cloaking area generated by Location Anonymization Server. Moreover, by using the improved LBS system model, this paper constructs a distributed framework, in which location privacy protection is wholly occupied in server side and LBS provider is only dedicated to QoS-guarantee. In addition, normalized privacy demand and QoS metrics are given and a user-defined weight parameter is introduced to ensure location privacy security without decreasing QoS. The feasibility of the proposed method is proved through simulation.

Privacy and Security Policies in Big Data

“…The tree-structure data release algorithms were proposed (Wang, 2015) to support a differential privacy budgeting strategy and reduce the query error, a series of methods based on tree-structure data split. As private spatial decompositions, these algorithms divide the geospatial data into smaller regions and for each of these regions, statistics are obtained on the points within.…”

“…Lastly, the time series data release algorithm is an example of methods used for applications with data such as MHR or GPS data. As explained in (Wang 2015): "The real time data with higher correlation between time stamps has a timescale, if the length of the time series is T and the noise noise t is added to the data x t at time k , noise : Lap T ( / ) ε , when T is large, the added noise gets large and leads to poor utility of the data." As for time series data, and for the purpose of reducing the error, the algorithm DFT was proposed based on discrete Fourier transform.…”