A robust reputation-based location-privacy recommender system using opportunistic networks

Location-sharing services have grown in use commensurately with the increasing popularity of smart phones. As location data can be sensitive, it is important to preserve people’s privacy while using such services, and so location-privacy recommender systems have been proposed to help people configure their privacy settings.These recommenders collect and store people’s data in a centralised system, but these themselves can introduce new privacy threats and concerns.In this paper, we propose a decentralised location-privacy recommender system based on opportunistic networks. We evaluate our system using real-world location-privacy traces, and introduce a reputation scheme based on encounter frequencies to mitigate the potential effects of shilling attacks by malicious users. Experimental results show that, after receiving adequate data, our decentralised recommender system’s performance is close to the performance of traditional centralised recommender systems (3% difference in accuracy and 1% difference in leaks). Meanwhile, our reputation scheme significantly mitigates the effect of malicious users’input (from 55% to 8% success) and makes it increasingly expensive to conduct such attacks.Postprin

Personalisation and recommender systems in digital libraries

Widespread use of the Internet has resulted in digital libraries that are increasingly used by diverse communities of users for diverse purposes and in which sharing and collaboration have become important social elements. As such libraries become commonplace, as their contents and services become more varied, and as their patrons become more experienced with computer technology, users will expect more sophisticated services from these libraries. A simple search function, normally an integral part of any digital library, increasingly leads to user frustration as user needs become more complex and as the volume of managed information increases. Proactive digital libraries, where the library evolves from being passive and untailored, are seen as offering great potential for addressing and overcoming these issues and include techniques such as personalisation and recommender systems. In this paper, following on from the DELOS/NSF Working Group on Personalisation and Recommender Systems for Digital Libraries, which met and reported during 2003, we present some background material on the scope of personalisation and recommender systems in digital libraries. We then outline the working group’s vision for the evolution of digital libraries and the role that personalisation and recommender systems will play, and we present a series of research challenges and specific recommendations and research priorities for the field

Recommending privacy preferences in location-sharing services

Location-sharing services have become increasingly popular with the proliferation of smartphones and online social networks. People share their locations with each other to record their daily lives or satisfy their social needs. At the same time, inappropriate disclosure of location information poses threats to people's privacy. One of the reasons why people fail to protect their location privacy is the difficulty of using the current mechanisms to manually configure location-privacy settings. Since people's location-privacy preferences are context-aware, manual configuration is cumbersome. People's incapability and unwillingness to do so lead to unexpected location disclosures that violate their location privacy. In this thesis, we investigate the feasibility of using recommender systems to help people protect their location privacy. We examine the performance of location-privacy recommender systems and compare it with the state-of-the-art. We also conduct online user studies to understand people's acceptance of such recommender systems and their concerns. We revise our design of the systems according to the results of the user studies. We find that user-based collaborative filtering can accurately recommend location-privacy preferences and outperform the state-of-the-art when training data are insufficient. From users' perspective, their acceptance of location-privacy recommender systems is affected by the openness and the context of recommendations and their privacy concerns about the systems. It is feasible to use data obfuscation or decentralisation to alleviate people's concerns and meanwhile keep the systems robust against malicious data attacks

Recommender Systems for Online and Mobile Social Networks: A survey

Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area