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Social Network De-anonymization: More Adversarial Knowledge, More Users Re-Identified?
[article]
2017
arXiv
pre-print
Preserved Fulltext
Previous work in social network de-anonymization mostly focuses on designing accurate and efficient de-anonymization methods. ...
As a result, users' privacy could be exposed to malicious third parties since they are extremely vulnerable to de-anonymization attacks, i.e., the attacker links the anonymous nodes in the social network ...
Social Network De-anonymization Social network de-anonymization [4] , [5] refers to the process of re-identifying anonymous nodes in a released social network. ...
arXiv:1710.10998v1
fatcat:7odfoqjp35htzd4mmifdq7ewie
De-anonymizing Social Networks
2009
2009 30th IEEE Symposium on Security and Privacy
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site, can be re-identified in the anonymous Twitter graph with only a 12% error rate. ...
Our de-anonymization algorithm is based purely on the network topology, does not require creation of a large number of dummy "sybil" nodes, is robust to noise and all existing defenses, and works even ...
The first author is grateful to Cynthia Dwork for introducing him to the problem of anonymity in social networks. ...
doi:10.1109/sp.2009.22
dblp:conf/sp/NarayananS09
fatcat:t5kvwlkcaffjznerid6n55xlia
Blind De-anonymization Attacks using Social Networks
[article]
2018
arXiv
pre-print
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., name, social security number) were removed and advanced data perturbation techniques were applied, several de-anonymization attacks have been proposed to re-identify individuals. ...
However, existing attacks have some limitations: 1) they are limited in de-anonymization accuracy; 2) they require prior seed knowledge and suffer from the imprecision of such seed information. ...
[25] proposed to de-anonymize a set of location traces based on a social network. ...
arXiv:1801.05534v1
fatcat:gs5prkdy6ffgnhqxxhqw4csed4
Community-Enhanced De-anonymization of Online Social Networks
2014
Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security - CCS '14
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However, by using external information such as a reference social graph (from the same network or another network with similar users), researchers have shown how such datasets can be de-anonymized. ...
To profit from their data while honoring the privacy of their customers, social networking services share 'anonymized' social network datasets, where, for example, identities of users are removed from ...
Social network deanonymization usually concerns the problem of cross-referencing two or more social graphs to enrich anonymous users' profiles and re-identify them. ...
doi:10.1145/2660267.2660324
dblp:conf/ccs/NilizadehKA14
fatcat:2tq6pdhq7fcbbbwazoval2tcru
Blind De-anonymization Attacks using Social Networks
2017
Proceedings of the 2017 on Workshop on Privacy in the Electronic Society - WPES '17
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., name, social security number) were removed and advanced data perturbation techniques were applied, several de-anonymization attacks have been proposed to re-identify individuals. ...
However, existing attacks have some limitations: 1) they are limited in de-anonymization accuracy; 2) they require prior seed knowledge and suffer from the imprecision of such seed information. ...
[22] proposed to de-anonymize a set of location traces based on a social network. ...
doi:10.1145/3139550.3139562
dblp:conf/wpes/LeeLJML17
fatcat:pl3r3zhppfcd7auo2frwixxh2e
Friend in the Middle (FiM): Tackling de-anonymization in social networks
2013
2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)
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Recently proposed deanonymization techniques proved to be effective in re-identifying users in anonymized social network. ...
In this paper, we present Friend in the Middle (FiM): a novel approach to make OSNs more resilient against de-anonymization techniques. ...
In fact, de-anonymization techniques [18] , [19] , [20] for social network have been also proposed. Hence, users anonymized with solutions as VPSN [4] might be easily re-identified. ...
doi:10.1109/percomw.2013.6529495
dblp:conf/percom/BeatoCP13
fatcat:w3q7ifmykneo5ckdifvmo4ax4a
Quantification of De-anonymization Risks in Social Networks
[article]
2017
arXiv
pre-print
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Several de-anonymization attacks have been proposed to re-identify individuals even if data anonymization techniques were applied. ...
and the potential of more powerful de-anonymization attacks in the future. ...
(Srivatsa and Hicks, 2012) proposed to de-anonymize a set of location traces based on a social network. ...
arXiv:1703.04873v1
fatcat:acaja7rg3nf4zmylpa4pka67o4
An Efficient and Robust Social Network De-anonymization Attack
[article]
2016
arXiv
pre-print
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2016
Proceedings of the 2016 ACM on Workshop on Privacy in the Electronic Society - WPES'16
unpublished
doi:10.1145/2994620.2994632 fatcat:oadz3tkm2zarrd4r3zezsemctq
doi:10.1145/2994620.2994632 fatcat:oadz3tkm2zarrd4r3zezsemctq
In our work, we consider structural social network de-anonymization attacks, which are used when a malicious party uses connections in a public or other identified network to re-identify users in an anonymized ...
In this paper we design and evaluate a novel social de-anonymization attack. ...
Beside several sucessful attacks on social networks [5, [13] [14] [15] [19] [20] [21] 27] , it has also been shown that location data can be de-anonymized with social networks as a background knowledge ...
arXiv:1610.04064v1
fatcat:aoq7rhv6zjajlcv2wcqz6mvmuy
Analysis of Grasshopper, a Novel Social Network De-anonymization Algorithm
2014
Periodica Polytechnica Electrical Engineering and Computer Science
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In addition to the benefits, serious privacy concerns also emerge: there are algorithms called de-anonymization attacks that are capable of re-identifying large fractions of anonymously published networks ...
A strong class of these attacks solely use the network structure to achieve their goals. In this paper we propose a novel structural de-anonymization attack called Grasshopper. ...
Furthermore, it should be investigated why we observed differences in recall between Epinions and others networks. ...
doi:10.3311/ppee.7878
fatcat:f56zkd37rrcu3ibhnxui4jgfb4
De-anonymizing Social Networks with Overlapping Community Structure
[article]
2017
arXiv
pre-print
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The advent of social networks poses severe threats on user privacy as adversaries can de-anonymize users' identities by mapping them to correlated cross-domain networks. ...
We jointly tackle above concerns under a more practical social network model parameterized by overlapping communities, which, neglected by prior art, can serve as side information for de-anonymization. ...
Although users can be anonymized by removing personal identifiers such as names and family addresses, it is not sufficient for privacy protection since adversaries may re-identify these users by correlated ...
arXiv:1712.04282v1
fatcat:f4gpxyugbnamtji6kh5qprwvdi
De-anonymization of Social Networks with Communities: When Quantifications Meet Algorithms
[article]
2017
arXiv
pre-print
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A crucial privacy-driven issue nowadays is re-identifying anonymized social networks by mapping them to correlated cross-domain auxiliary networks. ...
We address those concerns in a more realistic social network modeling parameterized by community structures that can be leveraged as side information for de-anonymization. ...
With the availability of only structural information, adversaries attempt to re-identify users by establishing a mapping between networks. ...
arXiv:1703.09028v3
fatcat:siowynetdrb53c47u776hsu7f4
Understanding structure-based social network de-anonymization techniques via empirical analysis
2018
EURASIP Journal on Wireless Communications and Networking
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In this paper, we conduct a comprehensive analysis on the typical structure-based social network de-anonymization algorithms. ...
However, de-anonymization techniques are actively studied to identify weaknesses in current social network data-publishing mechanisms. ...
As for passive attacks, colluding adversaries recognize their own sub-graph in anonymized graph which could re-identify users around them. Narayanan et al. ...
doi:10.1186/s13638-018-1291-2
fatcat:r3h5b4yaonczdjtqljwr3bzzty
De-anonymizing social networks and inferring private attributes using knowledge graphs
2016
IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications
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To protect against de-anonymization attack, various privacy protection techniques for social networks have been proposed. ...
Our experiment on data of real social networks shows that knowledge graphs can strengthen de-anonymization and inference attacks, and thus increase the risk of privacy disclosure. ...
Other previous methods focus on graph mapping attacks (also called structure-based de-anonymization), in which the attacker attempts to de-anonymize/re-identify users in the network, with only structural ...
doi:10.1109/infocom.2016.7524578
dblp:conf/infocom/QianLZC16
fatcat:57ml7kpwxvhodbynbfcgvfomgm
Social Network De-Anonymization and Privacy Inference with Knowledge Graph Model
2017
IEEE Transactions on Dependable and Secure Computing
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To protect against de-anonymization attack, various privacy protection techniques for social networks have been proposed. ...
Our experiment on data of real social networks shows that knowledge graphs can power de-anonymization and inference attacks, and thus increase the risk of privacy disclosure. ...
Other previous methods focus on graph mapping attacks (also called structure-based de-anonymization), in which the attacker attempts to de-anonymize/re-identify users in the network, with only structural ...
doi:10.1109/tdsc.2017.2697854
fatcat:g7p2dgdvxnb4hky63pv5kc7mlu
De-anonymization attack on geolocated data
2014
Journal of computer and system sciences (Print)
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We design several distance metrics quantifying the closeness between two MMCs and combine these distances to build de-anonymizers that can re-identify users in an anonymized geolocated dataset. ...
In this work, we focus on a specific inference attack called the de-anonymization attack, by which an adversary tries to infer the identity of a particular individual behind a set of mobility traces. ...
Their inference attack was able to successfully re-identify 35% of the population studied when the adversary has no auxiliary knowledge and even up to 50% when the adversary can use the knowledge of the ...
doi:10.1016/j.jcss.2014.04.024
fatcat:vd77z7rvordmvf36eomqj6jlta
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