Abstract
As a review system, the Crowd-Sourced Local Businesses Service System (CSLBSS) allows users to publicly publish reviews for businesses that include display name, avatar, and review content. While these reviews can maintain the business reputation and provide valuable references for others, the adversary also can legitimately obtain the user’s display name and a large number of historical reviews. For this problem, we show that the adversary can launch connecting user identities attack (CUIA) and statistical inference attack (SIA) to obtain user privacy by exploiting the acquired display names and historical reviews. However, the existing methods based on anonymity and suppressing reviews cannot resist these two attacks. Also, suppressing reviews may result in some reiews with the higher usefulness not being published. To solve these problems, we propose a cross-platform strong privacy protection mechanism (CSPPM) based on the partial publication and the complete anonymity mechanism. In CSPPM, based on the consistency between the user score and the business score, we propose a partial publication mechanism to publish reviews with the higher usefulness of review and filter false or untrue reviews. It ensures that our mechanism does not suppress reviews with the higher usefulness of reviews and improves system utility. We also propose a complete anonymity mechanism to anonymize the display name and avatars of reviews that are publicly published. It ensures that the adversary cannot obtain user privacy through CUIA and SIA. Finally, we evaluate CSPPM from both theoretical and experimental aspects. The results show that it can resist CUIA and SIA and improve system utility.
Highlights
With the development of position technology and the widespread use of smartphones, more and more social network applications provide Location-Based Services (LBSs), known as Location-Based Social Networks (LBSNs) [1], such as TripAdvisor, Yelp, Dianping
In Crowd-Sourced Local Businesses Service System (CSLBSS), both users and businesses desire as many highly credible reviews as possible to be published. e goal of the business is that more reviews will attract more consumers. e goal of the user is that more reviews will build more objective reputations for businesses while protecting their privacy. erefore, we evaluate our scheme with respect to three metrics: system utility, user utility, and privacy
We proposed a strong cross-platform privacy protection mechanism (CSPPM) based on the partial publication and complete anonymity mechanism to resist connecting user identities attack (CUIA) and statistical inference attack (SIA) on the scenario of review publication
Summary
With the development of position technology and the widespread use of smartphones, more and more social network applications provide Location-Based Services (LBSs), known as Location-Based Social Networks (LBSNs) [1], such as TripAdvisor, Yelp, Dianping. We can exploit these applications to socialize online, plan travel routes, have spatial crowdsourcing [2, 3], and query surrounding Point of Interests (POIs), which greatly facilitates our lives [4]. By browsing the list of reviews, consumers can get a true picture of the quality of the services provided by the business without going to the physical store
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