A Geo-Social Computing System (GSCS) allows users to declare their current locations, and uses these declared locations to make authorization decisions. Recent years have seen the emergence of a new generation of social computing systems that are GSCSs.This paper proposes a protection model for GSCSs. The protection system tracks the current locations of users and a knowledge base of primitive spatial relations between locations. Access control policies can be formulated by the composition of primitive spatial relations. The model is extended to account for Geo-Social Network Systems (GSNSs), which track both a spatial knowledge base and a social network. A policy language for GSNSs is proposed for specifying policies that combine both social and spatial constraints.

Nowadays, a user may belong to multiple social computing systems (SCSs) in order to benefit from a variety of services that each SCS may provide. To facilitate the sharing of contents across the system boundary, some SCSs provide a mechanism by which a user may "connect" his accounts on two SCSs. The effect is that contents from one SCS can now be shared to another SCS. Although such a connection feature delivers clear usability advantages for users, it also generates a host of privacy challenges. A notable challenge is that the access control policy of the SCS from which the content originates may not be honoured by the SCS to which the content migrates, because the latter fails to faithfully replicate the protection model of the former.In this paper we formulate a protection model for a federation of SCSs that support content sharing via account connection. A core feature of the model is that sharable contents are protected by access control policies that transcend system boundary -they are enforced even after contents are migrated from one SCS to another. To ensure faithful interpretation of access control policies, their evaluation involves querying the protection states of various SCSs, using Secure Multiparty Computation (SMC). An important contribution of this work is that we carefully formulate the conditions under which policy evaluation using SMC does not lead to the leakage of information about the protection states of the SCSs. We also study the computational problem of statically checking if an access control policy can be evaluated without information leakage. Lastly, we identify useful policy idioms.

Figure 1: The visualization of original (left) and anonymized (right) location-based social network (LBSN) data using GSUVis AbstractWe present GSUVis, a visualization tool designed to provide better understanding of location-based social network (LBSN) data. commonly reduces the utility of information available. Working with privacy experts, we designed GSUVis a visual analytic tool to help experts better understand the effects of anonymization techniques on LBSN data utility. One of GSUVis's primary goals is to make it possible for people to use LBSN data, without requiring them to gain deep knowledge about data anonymization. To inform the design of GSUVis, we interviewed privacy experts, and collected their tasks and system requirements. Based on this understanding, we designed and implemented GSUVis. It applies two anonymization algorithms for social and location trajectory data to a real-world LBSN dataset and visualizes the data both before and after anonymization. Through feedback from domain experts, we reflect on the effectiveness of GSUVis and the impact of anonymization using visualization.

No abstract

No abstract