Eran Toch

Empirical models of privacy in location sharing

The rapid adoption of location tracking and mobile social networking technologies raises significant privacy challenges. Today our understanding of peoples location sharing privacy preferences remains very limited, including how these preferences are impacted by the type of location tracking device or the nature of the locations visited. To address this gap, we deployed Locaccino, a mobile location sharing system, in a four week long field study, where we examined the behavior of study participants (n=28) who shared their location with their acquaintances (n=373.) Our results show that users appear more comfortable sharing their presence at locations visited by a large and diverse set of people. Our study also indicates that people who visit a wider number of places tend to also be the subject of a greater number of requests for their locations. Over time these same people tend to also evolve more sophisticated privacy preferences, reflected by an increase in time- and location-based restrictions. We conclude by discussing the implications our findings.

Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems

Personalization technologies offer powerful tools for enhancing the user experience in a wide variety of systems, but at the same time raise new privacy concerns. For example, systems that personalize advertisements according to the physical location of the user or according to the user’s friends’ search history, introduce new privacy risks that may discourage wide adoption of personalization technologies. This article analyzes the privacy risks associated with several current and prominent personalization trends, namely social-based personalization, behavioral profiling, and location-based personalization. We survey user attitudes towards privacy and personalization, as well as technologies that can help reduce privacy risks. We conclude with a discussion that frames risks and technical solutions in the intersection between personalization and privacy, as well as areas for further investigation. This frameworks can help designers and researchers to contextualize privacy challenges of solutions when designing personalization systems.

QSIA: a web-based environment for learning, assessing and knowledge sharing in communities

This paper describes a Web-based and distributed system named QSIA that serves as an environment for learning, assessing and knowledge sharing. QSIA - Questions Sharing and Interactive Assignments-offers a unified infrastructure for developing, collecting, managing and sharing of knowledge items. QSIA enhances collaboration in authoring via online recommendations and generates communities of teachers and learners. At the same time, QSIA fosters individual learning and might promote high-order thinking skills among its users. QSIAs community, conceptual architecture, structure overview and implementations are discussed.

A semantic approach to approximate service retrieval

Web service discovery is one of the main applications of semantic Web services, which extend standard Web services with semantic annotations. Current discovery solutions were developed in the context of automatic service composition. Thus, the “client” of the discovery procedure is an automated computer program rather than a human, with little, if any, tolerance to inexact results. However, in the real world, services which might be semantically distanced from each other are glued together using manual coding. In this article, we propose a new retrieval model for semantic Web services, with the objective of simplifying service discovery for human users. The model relies on simple and extensible keyword-based query language and enables efficient retrieval of approximate results, including approximate service compositions. Since representing all possible compositions and all approximate concept references can result in an exponentially-sized index, we investigate clustering methods to provide a scalable mechanism for service indexing. Results of experiments, designed to evaluate our indexing and query methods, show that satisfactory approximate search is feasible with efficient processing time.

Caché: caching location-enhanced content to improve user privacy

We present the design, implementation, and evaluation of Caché, a system that offers location privacy for certain classes of location-based applications. The core idea in Caché is to periodically pre-fetch potentially useful location-enhanced content well in advance. Applications then retrieve content from a local cache on the mobile device when it is needed. This approach allows an end-user to make use of location-enhanced content while only revealing to third-party content providers a large geographic region rather than a precise location. In this paper, we present an analysis that examines tradeoffs in terms of storage, bandwidth, and freshness of data. We then discuss the design and implementation of an Android service embodying these ideas. Finally, we provide two evaluations of Caché. One measures the performance of our approach with respect to privacy and mobile content availability using real-world mobility traces. The other focuses on our experiences using Caché to enhance user privacy in three open source Android applications.

Locaccino: a privacy-centric location sharing application

Locaccino is a location sharing application designed to empower users to effectively control their privacy. It has been piloted by close to 2000 users and has been used by researchers as an experimental platform for conducting research on location-based social networks. Featured technologies include expressive privacy rule creation, detailed feedback mechanisms that help users understand their privacy, algorithms for analyzing privacy preferences, and clients for mobile computers and smartphone devices. In addition, variations of Locaccino are also being piloted as part of research on user-controllable policy learning, learning usable privacy personas and reconciling expressiveness and user burden. The purpose of this demo is to introduce participants to the features of Locaccino, so that they can try out the Locaccino smartphone and laptop applications on their own devices, locate their friends and colleagues, and set rich privacy policies for sharing their location.

Retrospective privacy: managing longitudinal privacy in online social networks

Online social networks provide access to the users information for long periods of time after the informations initial publication. In this paper, we investigate the relation between information aging and its sharing preferences on Facebook. Our findings are based on a survey of 193 Facebook users, in which we asked users to specify their sharing preferences and intentions towards posts that were published in different periods of time (from the time of the survey and up to 24 months prior to the time of the survey.) Our results show that willingness to share significantly drops with the time passed since publishing the post. The occurrence of life changes, such as graduating from college or moving to a new town, is correlated with a further decrease in the willingness to share. We discuss our findings by relating it to information aging theories and privacy theories. Finally, we use our results to reflect on privacy mechanisms for long-term usage of online social networks, such as expiry date for content and historical information reviewing processes.

Generating default privacy policies for online social networks

Default privacy policies have a significant impact on the overall dynamics and success of online social networks, as users tend to keep their initial privacy policies. In this work-in-progress, we present a new method for suggesting privacy policies for new users by exploring knowledge of existing policies. The defaults generation process performs a collaborative analysis of the policies, finding personalized and representative suggestions. We show how the process can be extended to a wide range of domains, and present results based on 543 privacy policies obtained from a live location-based social network. Finally, we present a user interaction model that lets the user retain control over the default policies, allowing the user to make knowledgeable decisions regarding which default policy to take.

Who's your best friend?: targeted privacy attacks In location-sharing social networks

This paper presents a study that aims to answer two important questions related to targeted location-sharing privacy attacks: (1) given a group of users and their social graph, is it possible to predict which among them is likely to reveal most about their whereabouts, and (2) given a user, is it possible to predict which among her friends knows most about her whereabouts. To answer these questions we analyse the privacy policies of users of a real-time location sharing application, in which users actively shared their location with their contacts. The results show that users who are central to their network are more likely to reveal most about their whereabouts. Furthermore, we show that the friend most likely to know the whereabouts of a specific individual is the one with most common contacts and/or greatest number of contacts.

Locality and privacy in people-nearby applications

People-Nearby applications are becoming a popular way for individuals to search for new social relations in their physical vicinity. This paper presents the results of a qualitative study, based on 25 interviews, examining how privacy and locality are managed in these applications. We describe how location is used as a grounding mechanism, providing a platform for honest and truthful signals in the challenging process of forming new social relations. We discuss our findings by suggesting theoretical frameworks that can be used to analyze the social space induced by the applications, as well as to inform the design of new technologies that foster the creation of new social ties.