Mapping User Preference to Privacy Default Settings

Watson, Jason; Lipford, Heather Richter; Besmer, Andrew

doi:10.1145/2811257

Cited by 49 publications

(27 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, users could define a default response to the solutions suggested, e.g., always accept the suggested solution without asking me 9 , which, as shown in the evaluation (Section 6), would actually match user behaviour very accurately. Other suitable defaults could be applied based on approaches like [39], [40], [41], or users' responses could be (semi-)automated based on the concession rules instantiated in each situation, using any of the machine-learning approaches shown to work very well in social media privacy settings [18], [19].…”

Section: Discussionmentioning

confidence: 99%

Resolving Multi-Party Privacy Conflicts in Social Media

Such

Criado

2016

IEEE Trans. Knowl. Data Eng.

105

View full text Add to dashboard Cite

Items shared through Social Media may affect more than one user's privacy -e.g., photos that depict multiple users, comments that mention multiple users, events in which multiple users are invited, etc. The lack of multi-party privacy management support in current mainstream Social Media infrastructures makes users unable to appropriately control to whom these items are actually shared or not. Computational mechanisms that are able to merge the privacy preferences of multiple users into a single policy for an item can help solve this problem. However, merging multiple users' privacy preferences is not an easy task, because privacy preferences may conflict, so methods to resolve conflicts are needed. Moreover, these methods need to consider how users' would actually reach an agreement about a solution to the conflict in order to propose solutions that can be acceptable by all of the users affected by the item to be shared. Current approaches are either too demanding or only consider fixed ways of aggregating privacy preferences. In this paper, we propose the first computational mechanism to resolve conflicts for multi-party privacy management in Social Media that is able to adapt to different situations by modelling the concessions that users make to reach a solution to the conflicts. We also present results of a user study in which our proposed mechanism outperformed other existing approaches in terms of how many times each approach matched users' behaviour.

show abstract

Section: Discussionmentioning

confidence: 99%

Resolving Multi-Party Privacy Conflicts in Social Media

Such

Criado

2016

IEEE Trans. Knowl. Data Eng.

105

View full text Add to dashboard Cite

show abstract

“…Automatically inferring privacy needs from usage data Social network sites offer a variety of tools that allow an individual to set disclosure rules. Previous research has explored various methods for improving the understanding of complex privacy settings in SNSs (Watson et al, 2015). A recent study describes a privacy wizard for SNSs that describes a particular user's privacy preferences based on a limited amount of user input (Fang and LeFevre, 2010).…”

Section: Social Signalsmentioning

confidence: 99%

Phones, privacy, and predictions

Ghosh

Singh

2018

OIR

View full text Add to dashboard Cite

Purpose Mobile phones have become one of the most favored devices to maintain social connections as well as logging digital information about personal lives. The privacy of the metadata being generated in this process has been a topic of intense debate over the last few years, but most of the debate has been focused on stonewalling such data. At the same time, such metadata is already being used to automatically infer a user’s preferences for commercial products, media, or political agencies. The purpose of this paper is to understand the predictive power of phone usage features on individual privacy attitudes. Design/methodology/approach The present study uses a mixed-method approach, involving analysis of mobile phone metadata, self-reported survey on privacy attitudes and semi-structured interviews. This paper analyzes the interconnections between user’s social and behavioral data as obtained via their phone with their self-reported privacy attitudes and interprets them based on the semi-structured interviews. Findings The findings from the study suggest that an analysis of mobile phone metadata reveals vital clues to a person’s privacy attitudes. This study finds that multiple phone signals have significant predictive power on an individual’s privacy attitudes. The results motivate a newer direction of automatically inferring a user’s privacy attitudes by leveraging their phone usage information. Practical implications An ability to automatically infer a user’s privacy attitudes could allow users to utilize their own phone metadata to get automatic recommendations for privacy settings appropriate for them. This study offers information scientists, government agencies and mobile app developers, an understanding of user privacy needs, helping them create apps that take these traits into account. Originality/value The primary value of this paper lies in providing a better understanding of the predictive power of phone usage features on individual privacy attitudes.

show abstract

“…Similarly, in the area of smartphone app permissions, Liu et al [49] show that three profiles may be sufficient to capture users' permission preferences (they later developed an approach with 7 profiles [50]). Finally, in an SNS context, Fang and LeFevre [51] demonstrate how a "privacy wizard" can simplify privacy settings in a way that is simple to understand and use, while Watson et al [52] find that using multiple default settings does not significantly improve their fit beyond a single, optimized default setting.…”

Section: User-tailored Privacymentioning

confidence: 99%

(Work in Progress) User-Tailored Privacy by Design

Wilkinson¹,

Sivakumar²,

Cherry³

et al. 2017

Proceedings 2017 Workshop on Usable Security

View full text Add to dashboard Cite

The "privacy by design" philosophy addresses privacy aspects early in the design and development of an information system. While privacy by design solutions often provide considerable advantages over "post hoc" privacy solutions, they are usually not customized to the needs of individual users. Further, research shows that users differ substantially in their privacy management strategies. Thus, how can we support such broad privacy needs in a comprehensive and user-centered way? This paper presents the idea of user-tailored privacy by design, a design methodology that combines multiple privacy features into a single intelligent user interface. We discuss how this methodology moves beyond the "one-size-fits-all" approach of existing privacy by design solutions and the narrow focus on information disclosure of existing user-tailored privacy solutions. We illustrate our approach through an implementation of usertailored privacy by design within Facebook based on six privacy management profiles that were discovered in recent work, and subsequently extend this idea to the context of the Total Learning Architecture (TLA), which is a next generation learning platform that uses pervasive user monitoring to provide highly adaptive learning recommendations.

show abstract

Mapping User Preference to Privacy Default Settings

Cited by 49 publications

References 28 publications

Resolving Multi-Party Privacy Conflicts in Social Media

Resolving Multi-Party Privacy Conflicts in Social Media

Phones, privacy, and predictions

(Work in Progress) User-Tailored Privacy by Design

Contact Info

Product

Resources

About