unFriendly: Multi-party Privacy Risks in Social Networks

Thomas, Kurt; Grier, Chris; Nicol, David M.

doi:10.1007/978-3-642-14527-8_14

Cited by 94 publications

(107 citation statements)

References 13 publications

Supporting

Mentioning

102

Contrasting

Order By: Relevance

“…Jernigan and Mistree [2] demonstrated a method for accurately predicting the sexual orientation of Facebook users by analysing friendship associations. Thomas et al examine scenarios where conflicting privacy settings between friends will reveal information that at least one user intended remain private [15].…”

Section: Related Workmentioning

confidence: 99%

Estimating age privacy leakage in online social networks

Dey

Tang

Ross

et al. 2012

2012 Proceedings IEEE INFOCOM

View full text Add to dashboard Cite

Abstract-We perform a large-scale study to quantify just how severe the privacy leakage problem is in Facebook. As a case study, we focus on estimating birth year, which is a fundamental human attribute and, for many people, a private one. Specifically, we attempt to estimate the birth year of over 1 million Facebook users in New York City. We examine the accuracy of estimation procedures for several classes of users: (i) highly private users, who do not make their friend lists public; (ii) users who hide their birth years but make their friend lists public.To estimate Facebook users' ages, we exploit the underlying social network structure to design an iterative algorithm, which derives age estimates based on friends' ages, friends of friends' ages, and so on. We find that for most users, including highly private users who hide their friend lists, it is possible to estimate ages with an error of only a few years. We also make a specific suggestion to Facebook which, if implemented, would greatly reduce privacy leakages in its service.

show abstract

Section: Related Workmentioning

confidence: 99%

Estimating age privacy leakage in online social networks

Dey

Tang

Ross

et al. 2012

2012 Proceedings IEEE INFOCOM

View full text Add to dashboard Cite

show abstract

“…In addition, even when users choose to keep most of their information private, several attacks have shown that it is possible to infer sensitive attributes despite restrictive privacy settings [125,126]. This is made worse by the fact that information about a user is often revealed by the user's friends and family, resulting in a situation of interdependent privacy, i.e., where a user's privacy depends on other people [127].…”

Section: Privacy In Social Networkmentioning

confidence: 99%

Social Internet of Vehicles for Smart Cities

Μαγλαράς

Al-Bayatti

et al. 2016

JSAN

135

View full text Add to dashboard Cite

Digital devices are becoming increasingly ubiquitous and interconnected. Their evolution to intelligent parts of a digital ecosystem creates novel applications with so far unresolved security issues. A particular example is a vehicle. As vehicles evolve from simple means of transportation to smart entities with new sensing and communication capabilities, they become active members of a smart city. The Internet of Vehicles (IoV) consists of vehicles that communicate with each other and with public networks through V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure) and V2P (vehicle-to-pedestrian) interactions, which enables both the collection and the real-time sharing of critical information about the condition on the road network. The Social Internet of Things (SIoT) introduces social relationships among objects, creating a social network where the participants are not humans, but intelligent objects. In this article, we explore the concept of the Social Internet of Vehicles (SIoV), a network that enables social interactions both among vehicles and among drivers. We discuss technologies and components of the SIoV, possible applications and issues of security, privacy and trust that are likely to arise.

show abstract

“…Thomas et al [37] examine how the lack of joint privacy controls can put a user's privacy at risk. Notably, they highlight the inherent interdependent privacy risks due to friends in Facebook, and the fact that a user had no control over his friends' friend lists.…”

Section: Privacy Issues In Osnsmentioning

confidence: 99%

“…An obvious solution, already advanced in [37], is to set the visibility policy as the intersection of visibility policies selected by all users involved in the published information. Although it is difficult to force a friend to change his privacy settings on his personal attributes, it is possible to enforce his social links' privacy policy.…”

Section: Countermeasuresmentioning

confidence: 99%

Nowhere to Hide: Navigating around Privacy in Online Social Networks

Humbert¹,

Studer²,

Grossglauser³

et al. 2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In this paper, we introduce a navigation privacy attack, where an external adversary attempts to find a target user by exploiting publicly visible attributes of intermediate users. If such an attack is successful, it implies that a user cannot hide simply by excluding himself from a central directory or search function. The attack exploits the fact that most attributes (such as place of residence, age, or alma mater) tend to correlate with social proximity, which can be exploited as navigational cues while crawling the network. The problem is exacerbated by privacy policies where a user who keeps his profile private remains nevertheless visible in his friends' "friend lists"; such a user is still vulnerable to our navigation attack. Experiments with Facebook and Google+ show that the majority of users can be found efficiently using our attack, if a small set of attributes are known about the target as side information. Our results suggest that, in an online social network where many users reveal a (even limited) set of attributes, it is nearly impossible for a specific user to "hide in the crowd".

show abstract

unFriendly: Multi-party Privacy Risks in Social Networks

Cited by 94 publications

References 13 publications

Estimating age privacy leakage in online social networks

Estimating age privacy leakage in online social networks

Social Internet of Vehicles for Smart Cities

Nowhere to Hide: Navigating around Privacy in Online Social Networks

Contact Info

Product

Resources

About