Towards understanding residential privacy by analyzing users' activities in foursquare

Jin, Lei; Long, Xuelian; Joshi, James

doi:10.1145/2382416.2382428

Cited by 12 publications

(7 citation statements)

References 10 publications

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“…The Location-Based Social Network (LBSN) Foursquare was chosen as a platform for this study. It is a fairly popular LBSN that provides a typical example of GeoSNs, and as such has been used in several previous studies in the literature [21,27,51,52].Using a public GeoSN for evaluation provides more accurate insights of the general user's privacy attitude and bahaviour rather than using restricted location-sharing applications (e.g., [3][4][5]). Foursquare offers place discovery and recommendation services based on users' location and previous visits to places (check-ins).…”

Section: Methodsmentioning

confidence: 99%

Towards Understanding Location Privacy Awareness on Geo-Social Networks

Abdelmoty

Alrayes

2017

IJGI

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Users' awareness of the extent of information implicit in their geo-profiles on social networks is limited. This questions the validity of their consent to the collection, storage and use of their data. Tools for location privacy awareness are needed that provide users with accessible means for understanding the implicit content in their location information as well as a view of the level of risk to their privacy as a consequence of disclosing this information. Towards this goal, an abstract model of location privacy threat levels is first derived from a user study involving 186 users. This is then used to inform the design of a prototype privacy feedback tool for a location-based social network. Another user study involving 338 users of this network is carried out to test the effectiveness of the proposed design. Findings confirm the strong need of users for more transparent access to and control over their location profiles and guide the proposal of recommendations to the design of more privacy-sensitive geo-social networks.

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Section: Methodsmentioning

confidence: 99%

Towards Understanding Location Privacy Awareness on Geo-Social Networks

Abdelmoty

Alrayes

2017

IJGI

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“…Paper(s) RQ1 RQ2 RQ3 RQ4 RQ5 RQ6 RQ7 RQ8 RQ9 [7] [18] [86,88,97,176,193] [108] [79,86,139,186] [189]…”

Section: Periodmentioning

confidence: 99%

A Survey of User Perspectives on Security and Privacy in a Home Networking Environment

Nandita

Nurse

2023

ACM Comput. Surv.

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The security and privacy of smart home systems, particularly from a home user’s perspective, have been a very active research area in recent years. However, via a meta-review of 52 review papers covering related topics (published between 2000 and 2021), this paper shows a lack of a more recent literature review on user perspectives of smart home security and privacy since the 2010s. This identified gap motivated us to conduct a systematic literature review (SLR) covering 126 relevant research papers published from 2010 to 2021. Our SLR led to the discovery of a number of important areas where further research is needed; these include holistic methods that consider a more diverse and heterogeneous range of home devices, interactions between multiple home users, complicated data flow between multiple home devices and home users, some less-studied demographic factors, and advanced conceptual frameworks. Based on these findings, we recommended key future research directions, e.g., research for a better understanding of security and privacy aspects in different multi-device and multi-user contexts, and a more comprehensive ontology on the security and privacy of the smart home covering varying types of home devices and behaviors of different types of home users.

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“… D3: It is a Foursquare friend network dataset used in [11]. This network is a well-connected network.…”

Section: Datasetsmentioning

confidence: 99%

Exploiting Users' Inconsistent Preferences in Online Social Networks to Discover Private Friendship Links

Jin

Takabi

Long

et al. 2014

Proceedings of the 13th Workshop on Privacy in the Electronic Society

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In a social network system, a friendship relation between two users is usually represented by an undirected link and it is visible in both users' friend lists. Such a dual visibility of a friendship link may raise privacy threats. This is because both the users of a friendship link can separately control its visibility to other users and their preferences of sharing such a friendship link may not be consistent. Even if one of them conceals the friendship link from a third user, that third user may find the link through the other user's friend list. In addition, as most social network users allow their friends to see their friend lists, an adversary can exploit these inconsistent policies caused by users' conflicting preferences to identify and infer many of a targeted user's friends and even reconstruct the topology of an entire social network. In this paper, we propose, characterize and evaluate such an attack referred as the Friendship Identification and Inference (FII) attack. In an FII attack scenario, an adversary first accumulates the initial attack relevant information based on the friend lists visible to him in a social network. Then, he utilizes this information to identify and infer a target's friends using a random walk based approach. We formally define the attack and present the attack steps, the attack algorithm and various attack schemes. Our experimental results using three real social network datasets show that FII attacks are effective in inferring private friendship links of a target and predicting the topology of the social network. Currently, most popular social network systems, such as Facebook, LinkedIn and Foursquare, are susceptible to FII attacks.

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Towards understanding residential privacy by analyzing users' activities in foursquare

Cited by 12 publications

References 10 publications

Towards Understanding Location Privacy Awareness on Geo-Social Networks

Towards Understanding Location Privacy Awareness on Geo-Social Networks

A Survey of User Perspectives on Security and Privacy in a Home Networking Environment

Exploiting Users' Inconsistent Preferences in Online Social Networks to Discover Private Friendship Links

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