Estimating age privacy leakage in online social networks

Dey, Ratan; Tang, Cong; Ross, Keith W.; Saxena, Nitesh

doi:10.1109/infcom.2012.6195711

Cited by 74 publications

(68 citation statements)

References 10 publications

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“…Some information from user or neighbor profiles could indirectly identify specific attributes of OSN users. Dey et al (2012) show that it is possible to estimate the age of users on Facebook by analyzing friend's ages, ages of friends of friends, and so on. They show it is possible to estimate ages with an error of a few years for highly private users who do not share friend list in public.…”

Section: Neighborhood Attacksmentioning

confidence: 99%

A Survey of Social Network Forensics

Karabiyik¹,

Canbaz²,

Aksoy³

et al. 2016

JDFSL

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Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models/techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks.

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Section: Neighborhood Attacksmentioning

confidence: 99%

A Survey of Social Network Forensics

Karabiyik¹,

Canbaz²,

Aksoy³

et al. 2016

JDFSL

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“…Henne et al study how OSN pictures uploaded by friends can reveal information about one's own location [13]. Dey et al analyze the risk of age inference in OSNs, notably by relying on information posted by users' friends and friends-of-friends [7]. In the context of location privacy, Vratonjic et al show how mobile users connecting to location-based services from the same IP address can indirectly compromise the location privacy of others [27].…”

Section: Related Workmentioning

confidence: 99%

On Non-cooperative Genomic Privacy

Humbert

Ayday

Hubaux

et al. 2015

Financial Cryptography and Data Security

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Abstract. Over the last few years, the vast progress in genome sequencing has highly increased the availability of genomic data. Today, individuals can obtain their digital genomic sequences at reasonable prices from many online service providers. Individuals can store their data on personal devices, reveal it on public online databases, or share it with third parties. Yet, it has been shown that genomic data is very privacysensitive and highly correlated between relatives. Therefore, individuals' decisions about how to manage and secure their genomic data are crucial. People of the same family might have very different opinions about (i) how to protect and (ii) whether or not to reveal their genome. We study this tension by using a game-theoretic approach. First, we model the interplay between two purely-selfish family members. We also analyze how the game evolves when relatives behave altruistically. We define closed-form Nash equilibria in different settings. We then extend the game to N players by means of multi-agent influence diagrams that enable us to efficiently compute Nash equilibria. Our results notably demonstrate that altruism does not always lead to a more efficient outcome in genomic-privacy games. They also show that, if the discrepancy between the genome-sharing benefits that players perceive is too high, they will follow opposite sharing strategies, which has a negative impact on the familial utility.

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“…They propose 3 different attacks: friend-list, profile and wall-post recovery attacks. Dey et al [8] estimate the leakage of age information in Facebook, either by relying on the target's profile directly, or by using information released by the targets' friends.…”

Section: Privacy Issues In Osnsmentioning

confidence: 99%

“…We assume this attacker to have prior knowledge on the values of a subset A t of t's personal attributes, that he will use to navigate towards the target. As the attacker will reach the target through the target's social links (friends, friends of friends, ...), he will also discover at least one friend of the target, which can be useful for friend-based inference attacks [8,33,42]. Finally, note that the attacker we consider in this work is passive, in that he does not subvert any user account or interact with other OSN users, e.g., to create social ties with them.…”

Section: Modelmentioning

confidence: 99%

“…In this paper, we evaluate the feasibility of such an attack for large networks and ultimately answer the following question: Is it possible to find a target profile by navigating a small fraction of the whole network, by relying on public attributes of queried profiles? Answering this question is crucial for privacy, because reaching the target profile or its neighborhood is the necessary precondition for any targeted attack such as the inference of hidden attributes (e.g., political or religious views) through other personal attributes [7,29], or through friends' public attributes [8,22,33].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nowhere to Hide: Navigating around Privacy in Online Social Networks

Humbert¹,

Studer²,

Grossglauser³

et al. 2013

Lecture Notes in Computer Science

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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".

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Estimating age privacy leakage in online social networks

Cited by 74 publications

References 10 publications

A Survey of Social Network Forensics

A Survey of Social Network Forensics

On Non-cooperative Genomic Privacy

Nowhere to Hide: Navigating around Privacy in Online Social Networks

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