User-Controllable Security and Privacy for Pervasive Computing

Cornwell, Jason; Fette, Ian; Hsieh, Gary; Prabaker, Madhu; Rao, Jinghai; Tang, Karen P.; Vaniea, Kami; Bauer, Lujo; Cranor, Lorrie Faith; Hong, Jason I.; McLaren, Bruce M.; Reiter, Mike; Sadeh, Norman

doi:10.1109/wmcsa.2007.4389552

Cited by 17 publications

(18 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This article extends a previous workshop paper in which we introduced PEOPLEFINDER [5], and provides a more thorough and detailed report of our user studies.…”

Section: Introductionmentioning

confidence: 59%

Understanding and capturing people’s privacy policies in a mobile social networking application

Sadeh

Hong

Cranor

et al. 2008

Pers Ubiquit Comput

Self Cite

263

191

View full text Add to dashboard Cite

A number of mobile applications have emerged that allow users to locate one another. However, people have expressed concerns about the privacy implications associated with this class of software, suggesting that broad adoption may only happen to the extent that these concerns are adequately addressed. In this article, we report on our work on PEOPLEFINDER, an application that enables cell phone and laptop users to selectively share their locations with others (e.g. friends, family, and colleagues). The objective of our work has been to better understand people's attitudes and behaviors towards privacy as they interact with such an application, and to explore technologies that empower users to more effectively and efficiently specify their privacy preferences (or "policies"). These technologies include user interfaces for specifying rules and auditing disclosures, as well as machine learning techniques to see if the system can help people manage their policies better. We present evaluations of these technologies in the context of one laboratory study and three field studies.

show abstract

“…This article extends a previous workshop paper in which we introduced PEOPLEFINDER [5], and provides a more thorough and detailed report of our user studies.…”

Section: Introductionmentioning

confidence: 59%

Understanding and capturing people’s privacy policies in a mobile social networking application

Sadeh

Hong

Cranor

et al. 2008

Pers Ubiquit Comput

Self Cite

263

191

View full text Add to dashboard Cite

show abstract

“…(iv) Designed Invasion (due to poorly designed features): A cell phone application that reveals location to friends, but does this without informing the user or providing control of this feature [15,16,17].…”

Section: Eai European Alliancementioning

confidence: 99%

How did you know that about me? Protecting users against unwanted inferences

Motahari¹,

Mayer²,

Jones³

2011

ICST Trans Secur Saf

View full text Add to dashboard Cite

The widespread adoption of social computing applications is transforming our world. It has changed the way we routinely communicate and navigate our environment and enabled political revolutions. However, despite these applications' ability to support social action, their use puts individual privacy at considerable risk. This is in large part due to the fact that the public sharing of personal information through social computing applications enables potentially unwanted inferences about users' identity, location, or other related personal information. This paper provides a systematic overview of the social inference problem. It highlights the public's and research community's general lack of awareness of the problem and associated risks to user privacy. A social inference risk prediction framework is presented and associated empirical studies that attest to its validity. This framework is then used to outline the major research and practical challenges that need to be addressed if we are to deploy effective social inference protection systems. Challenges examined include how to address the computational complexity of social inference risk modeling and designing user interfaces that inform users about social inference opportunities.

show abstract

“…Researchers found that people have significant privacy concerns when sharing their location with others [11,12,16,22,24,31,39]. Iachello et al argued that it is essential for applications to support plausible deniability when disclosing location information.…”

Section: Location Sharing Applicationsmentioning

confidence: 99%

“…Some recent applications provide users more control of their privacy preferences [39,42], such as the application mentioned by Cornwell et al [16], the later version of which is also called "Locaccino" [5]. Locaccino is a user-controllable location sharing tool which gives users control on selectively sharing their location.…”

Section: Location Sharing Applicationsmentioning

confidence: 99%

Understanding People's Place Naming Preferences in Location Sharing

Lin¹,

Hong²,

Sadeh³

2009

Self Cite

View full text Add to dashboard Cite

This is an updated version of CMU-ISR-09-121/CMU-CyLab-09-010.This work has been supported by NSF grants CNS-0627513 and CNS-0905562. Additional support has been provided by Nokia, France Telecom, Google, the CMU/Microsoft Center for Computational Thinking, ARO research grant DAAD19-02-1-0389 to Carnegie Mellon University's CyLab, and the CMU/Portugal Information and Communication Technologies Institute.Keywords: Location sharing, Location-based service, Location representation, Place naming. ABSTRACTMost location sharing applications display people's locations on a map. However, in practice, people use a rich variety of terms to refer to their locations when interacting with others, such as "home," "Starbucks," or "the bus stop near my house." Our long-term goal is to create a system that can automatically generate appropriate place names based on real-time context and user preferences. As a first step, we analyze data from a two-week study involving 26 participants in two different cities, focusing on how people refer to places in location sharing. We derive a taxonomy of different place naming methods, and show that factors such as a person's perceived familiarity with a place and the entropy of that place (i.e. the variety of people who visit it) strongly influence the way people refer to it when interacting with that person. We proceed with the description of a machine learning model for predicting how people name places. Using our data, this model is able to predict the place naming method people choose with an average accuracy higher than 85%.

show abstract

User-Controllable Security and Privacy for Pervasive Computing

Abstract: Abstract

Cited by 17 publications

References 3 publications

Understanding and capturing people’s privacy policies in a mobile social networking application

Understanding and capturing people’s privacy policies in a mobile social networking application

How did you know that about me? Protecting users against unwanted inferences

Understanding People's Place Naming Preferences in Location Sharing

Contact Info

Product

Resources

About