Publication statistics

Pub. period:2007-2012
Pub. count:8
Number of co-authors:25



Co-authors

Number of publications with 3 favourite co-authors:

Jason Hong:5
Norman Sadeh:5
Justin Cranshaw:3

 

 

Productive colleagues

Eran Toch's 3 most productive colleagues in number of publications:

Aniket Kittur:27
Jason Hong:20
Vassilis Kostakos:20
 
 
 

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Eran Toch

 

Publications by Eran Toch (bibliography)

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2012
 
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Toch, Eran and Levi, Inbal (2012): What can 'people-nearby' applications teach us about meeting new people?. In: Proceedings of the 2012 International Conference on Uniquitous Computing 2012. pp. 802-803.

'People-nearby' applications for meeting new people online are some of the most popular examples of systems that lead people from an online interaction to an offline interaction. This paper provides a critical review of available applications, and identifies three key properties that are essential for the applications: physical location, identity management, and trust. The paper suggests open research questions that can explain the success of these applications and guide the design of new technologies that encourage offline interactions.

© All rights reserved Toch and Levi and/or ACM Press

2011
 
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Kostakos, Vassilis, Venkatanathan, Jayant, Reynolds, Bernardo, Sadeh, Norman, Toch, Eran, Shaikh, Siraj A. and Jones, Simon (2011): Who's your best friend?: targeted privacy attacks in location-sharing social networks. In: Proceedings of the 2011 International Conference on Uniquitous Computing 2011. pp. 177-186. Available online

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.

© All rights reserved Kostakos et al. and/or ACM Press

2010
 
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Cranshaw, Justin, Toch, Eran, Hong, Jason, Kittur, Aniket and Sadeh, Norman (2010): Bridging the gap between physical location and online social networks. In: Proceedings of the 2010 International Conference on Uniquitous Computing 2010. pp. 119-128. Available online

This paper examines the location traces of 489 users of a location sharing social network for relationships between the users' mobility patterns and structural properties of their underlying social network. We introduce a novel set of location-based features for analyzing the social context of a geographic region, including location entropy, which measures the diversity of unique visitors of a location. Using these features, we provide a model for predicting friendship between two users by analyzing their location trails. Our model achieves significant gains over simpler models based only on direct properties of the co-location histories, such as the number of co-locations. We also show a positive relationship between the entropy of the locations the user visits and the number of social ties that user has in the network. We discuss how the offline mobility of users can have implications for both researchers and designers of online social networks.

© All rights reserved Cranshaw et al. and/or their publisher

 
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Toch, Eran, Cranshaw, Justin, Drielsma, Paul Hankes, Tsai, Janice Y., Kelley, Patrick Gage, Springfield, James, Cranor, Lorrie, Hong, Jason and Sadeh, Norman (2010): Empirical models of privacy in location sharing. In: Proceedings of the 2010 International Conference on Uniquitous Computing 2010. pp. 129-138. Available online

The rapid adoption of location tracking and mobile social networking technologies raises significant privacy challenges. Today our understanding of people's 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.

© All rights reserved Toch et al. and/or their publisher

 
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Toch, Eran, Cranshaw, Justin, Hankes-Drielsma, Paul, Springfield, Jay, Kelley, Patrick Gage, Cranor, Lorrie, Hong, Jason and Sadeh, Norman (2010): Locaccino: a privacy-centric location sharing application. In: Proceedings of the 2010 International Conference on Uniquitous Computing 2010. pp. 381-382. Available online

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.

© All rights reserved Toch et al. and/or their publisher

 
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Toch, Eran, Sadeh, Norman M. and Hong, Jason (2010): Generating default privacy policies for online social networks. In: Proceedings of ACM CHI 2010 Conference on Human Factors in Computing Systems 2010. pp. 4243-4248. Available online

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.

© All rights reserved Toch et al. and/or their publisher

2009
 
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Toch, Eran, Ravichandran, Ramprasad, Cranor, Lorrie, Drielsma, Paul, Hong, Jason, Kelley, Patrick, Sadeh, Norman and Tsai, Janice (2009): Analyzing use of privacy policy attributes in a location sharing application. In: Proceedings of the 2009 Symposium on Usable Privacy and Security 2009. p. 32. Available online

2007
 
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Toch, Eran, Gal, Avigdor, Reinhartz-Berger, Iris and Dori, Dov (2007): A semantic approach to approximate service retrieval. In ACM Trans. Internet Techn., 8 (1) . Available online

 
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