The Effect of Privacy Concerns on Privacy Recommenders

Abstract

Location-sharing services such as Facebook and Foursquare/Swarm have become increasingly popular, due to the ease at which users can share their locations, and participate in services, games and other applications that leverage these locations. But it is important for people who use these services to configure appropriate location-privacy preferences so that they can control to whom they want to share their location information. Manually configuring these preferences may be burdensome and confusing, and so location-privacy preference recommenders based on crowdsourcing preferences from other users have been proposed. Whether people will accept the recommended preferences acquired from other users, who they may not know or trust, has not, however, been investigated.In this paper, we present a user experiment (n=99) to explore what factors influence people's acceptance of locationprivacy preference recommenders. We find that 44% of our participants have privacy concerns about such recommenders. These concerns are shown to have a negative effect (p <0.001) on their acceptance of the recommendations and their satisfaction about their choices. Furthermore, users' acceptance of recommenders varies according to both context and recommendations being made. Our findings are potentially useful to designers of location-sharing services and privacy recommenders.

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

“…More importantly, it does not need the support of a central server. In our previous user study [42], we have found significant privacy concerns from users about providing their data to a central server and such concerns have negative effects on users' perceived recommendation quality, satisfaction, and acceptance of recommendations. Decentralisation enables us to remove the source of such concerns.…”

“…To alleviate the negative effects of people's privacy concerns about providing their data to a centralised recommender [42], in contrast to the existing works, we investigate the feasibility of deploying a location-privacy recommender in a decentralised fashion. In addition, compared with other proposed decentralised recommender systems [13,24,33], we demonstrate the vulnerability of decentralised recommender to sampling attacks, as explained in the next subsection, and propose a reputation scheme that mitigates the effectiveness of such attacks.…”

“…These are all based, however, on the structure of a centralised recommender system to which people contribute their personal data. User studies have shown that people have privacy concerns about providing their data to such a centralised location-privacy recommender system [42]. These concerns have negative effects on their satisfaction about their choices, their perceived recommendation quality, and their acceptance of the recommendations.…”

Modeling Dyslexic Students’ Motivation for Enhanced Learning in E-learning Systems

“…This has gone beyond the findings of prior research. For example, Zhao et al's study has revealed that users' subjective factor about perceived quality of system acts as a mediator between users' concern and acceptance of the system [88], but it was not illustrated what factors are included in the quality of the system. The mediators identified in our study combine the aspects of both users and e-learning systems, reflecting both system-specific features (i.e., confirmed fit, utilization) and user-specific experience (i.e., learning experience, reading experience).…”

Should I Agree?

“…Privacy intermediaries and recommendation-based systems for consent have been explored in [1,12,81] including for collecting consent decisions under a single interface [41,49], simplifying privacy policies [7] or automating decision making [4,41]. From our review, we identified four main categories of approach: First, community or crowd-based systems are either community-based projects such asTerms of Service; Didn't Read (ToS;DR) [63] or privacy intermediaries that use crowd-sourcing approaches to support users to make data permission decisions [42,45].…”