You are who you know: inferring user profiles in online social networks

By Alan Mislove, Bimal Viswanath, Krishna P. Gummadi, Peter Druschel. 2010

796 citations recorded by the source corpus.

Subfields: Communication, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

In Statistical and Nonlinear Physics

Citations in the corpus

Faceless Person Recognition: Privacy Implications in Social Media (2016, background): “Privacy of users in social network is a well studied topic in the security community [2, 3, 1, 4].”
Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks (2018, background): “In [12], the authors demonstrate that users’ sensitive”
Measuring Large-Scale Social Networks with High Resolution (2014, methodology): “online social networks) are used to re-identify users [73–75].”
SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection (2013, result): “[35] had equivalent results to using state-ofthe-art Sybil detection approaches.”
Learning to Discover Social Circles in Ego Networks (2012, background): “Secondly, we expect that each circle is not only densely connected but its members also share common properties or traits [18, 28].”
The ethics of smart cities and urban science (2016, background): “It has been calculated that knowing the sexual orientation of just 20% of social media users will enable the orientation of all other users to be inferred with a high degree of accuracy [53].”
Adversarial Image Perturbation for Privacy Protection A Game Theory Perspective (2017, background): “While there exists a bulk of research on user privacy traditionally led by the security community [22, 39, 23, 19], studies on private content in visual data began only recently [37, 28, 18].”
Aiding the Detection of Fake Accounts in Large Scale Social Online Services (2012, neutral): “In the rest, we discuss our findings and prior OSN Sybil defense proposals.”
An analysis of social network-based Sybil defenses (2010, methodology): “To do show this, we repeat the experiment above for two real world networks: Facebook, consisting of the social network between Rice University graduate students taken from Facebook [21], and Astrophysics, consisting of the coauthorship network between astrophysicists [25].”
Attribute Inference Attacks in Online Social Networks (2018, background): “One privacy attack of increasing interest revolves around these user attributes [10, 15, 23, 27, 35, 36, 40, 42, 46, 57, 60, 64].”
You Are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors (2016, background): “Existing attribute inference attacks can be roughly classified into two categories, friend-based [18, 27, 46, 39, 15, 33, 11, 26, 20, 21, 23] and behavior-based [42, 29, 8, 25].”
Secure Function Evaluation Using an FPGA Overlay Architecture (2017, background): “On the other hand, this massive data collection and mining has given rise to signiﬁcant privacy concerns, extensively documented by researchers [20, 24, 26, 30, 34, 36, 40] as well as the popular press [2, 42].”
WHATSUP: A Decentralized Instant News Recommender (2013, background): “) [19], it does not in the dynamic context of news dissemination (as shown by our cascading results).”
Exploiting Temporal Dynamics in Sybil Defenses (2015, background): “Researchers have found that honest users tend to form multiple small communities [17] driven by different purposes (e.”
Characterizing Silent Users in Social Media Communities (2015, methodology): “There are some user profiling methods that explore the use of social links and neighbors’ attributes (Mislove et al. 2010; Yang et al. 2011).”