Synonyms
Adversarial knowledge; Anonymity; Complexity; Graph algorithms; Privacy breach; Social network privacy
Glossary
- Adversary:
-
Somebody who attempts to reveal sensitive, private information
- Adversarial Model:
-
Formal description of the unique characteristics of a particular adversary
- Attribute Disclosure:
-
A privacy breach wherein some descriptive attribute of somebody is revealed
- Identity Disclosure:
-
A privacy breach in which a presumably anonymous person is in fact identifiable
- k-P-Anonymity:
-
A condition under which any instance of P appears at least k times
- Target:
-
The particular social network member against whom an adversary is trying to breach privacy
Definition
As social networks grow and become increasingly pervasive, so too do the opportunities to analyze the data that arises from them. Social network data can be released for public research that can lead to breakthroughs in fields as diverse as marketing and health care. But with the release of data come questions of...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aggarwal G, Feder T, Kenthapadi K, Motwani R, Panigrahy R, Thomas D, Zhu A (2005) Anonymizing tables. In: Proceedings of the ICDT, Edinburgh, pp 246–258
Backstrom L, Dwork C, Kleinberg JM (2007) Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography. In: Proceedings of the WWW, Banff, pp 181–190
Boldi P, Bonchi F, Gionis A, Tassa T (2012) Injecting uncertainty in graphs for identity obfuscation. PVLDB 5(11):1376–1387
Bonizzoni P, Vedova GD, Dondi R (2009) The k-anonymity problem is hard. In: Proceedings of the FCT, Wroclaw, pp 26–37
Casas-Roma J, Herrera-Joancomart J, Torra V (2012) Comparing random-based and k-anonymity-based algorithms for graph anonymization. In: Proceedings of the MDAI, Girona. Springer, pp 197–209
Cheng J, Fu AW-C, Liu J (2010) K-isomorphism: privacy preserving network publication against structural attacks. In: Proceedings of the SIGMOD, Indianapolis, pp 459–470
Chester S, Srivastava G (2011) Social network privacy for attribute disclosure attacks. In: Proceedings of the ASONAM, Kaohsiung, pp 445–449
Chester S, Gaertner J, Stege U, Venkatesh S (2012a) Anonymizing subsets of social networks with degree constrained subgraphs. In: Proceedings of the ASONAM, Istanbul, pp 418–422
Chester S, Kapron BM, Ramesh G, Srivastava G, Thomo A, Venkatesh S (2012b) Why Waldo befriended the dummy? k-anonymization of social networks with pseudo-nodes. Soc Netw Anal Min, 3(3):381–399
Chester S, Kapron BM, Srivastava G, Venkatesh S (2012c) Complexity of social network anonymization. Soc Netw Anal Min, 3(2):151–166
Dwork C (2008) Differential privacy: a survey of results. In: Proceedings of the TAMC, Xi'an, pp 1–19
Erdos P, Gallai T (1960) Gráfok elöÃrt fokszámú pontokkal. Matematikai Lapok 11:264–274
Hay M, Miklau G, Jensen D, Weis P, Srivastava S (2007) Anonymizing social networks. Amherst technical report, University of Massachusetts
Hay M, Li C, Miklau G, Jensen D (2009) Accurate estimation of the degree distribution of private networks. In: Proceedings of the ICDM 2009, Miami, pp 169–178
Karwa V, Raskhodnikova S, Smith A, Yaroslavtsev G (2011) Private analysis of graph structure. PVLDB 4(11):1146–1157
Kasiviswanathan SP, Nissim K, Raskhodnikova S, Smith A (2013) Analyzing graphs with node differential privacy. In: Proceedings of the TCC, Tokyo, pp 457–476
Li N, Li T, Venkatasubramanian S (2007) t-closeness: privacy beyond k-anonymity and l-diversity. In: Proceedings of the ICDE, Istanbul, pp 106–115
Lui K, Terzi E (2008) Towards identity anonymization on graphs. In: Proceedings of the SIGMOD, Vancouver, pp 93–106
Machanavajjhala A, Kifer D, Gehrke J, Venkitasubramaniam M (2007) L-diversity: privacy beyond k-anonymity. TKDD 1(1):52
Meyerson A, Williams R (2004) On the complexity of optimal K-anonymity. In: Proceedings of the PODS, Paris, pp 223–228
Narayanan A, Shmatikov V (2009) De-anonymizing social networks. In: Proceedings of IEEE symposium on security and privacy, Oakland, pp 173–187
Nissim K, Raskhodnikova S, Smith A (2007) Smooth sensitivity and sampling in private data analysis. In: Proceedings of the STOC, San Diego, pp 75–84
Srivatsa M, Hicks M (2012) Deanonymizing mobility traces: using social network as a side-channel. In: Proceedings of the ACM conference on computer and communications security, Raleigh, pp 628–637
Sweeney L (2002) k-anonymity: a model for protecting privacy. Int J Uncertain Fuzziness Knowl Based Syst 10(5):557–570
Thompson B, Yao D (2009) The union-split algorithm and cluster-based anonymization of social networks. In: Proceedings of the ASIACCS, Sydney, pp 218–227
Wu W, Xiao Y, Wang W, He Z, Wang Z (2010) k-symmetry model for identity anonymization in social networks. In: Proceedings of the EDBT, Lausanne, pp 111–122
Ying X, Pan K, Wu X, Guo L (2009) Comparisons of randomization and K-degree anonymization schemes for privacy preserving social network publishing. In: Proceedings of the SNA-KDD, Paris. Article #10, 10 pages
Zhou B, Pei J (2008) Preserving privacy in social networks against neighborhood attacks. In: Proceedings of the ICDE, Cancun, pp 506–515
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this entry
Cite this entry
Chester, S., Kapron, B.M., Srivastava, G., Srinivasan, V., Thomo, A. (2014). Anonymization and De-anonymization of Social Network Data. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6170-8_22
Download citation
DOI: https://doi.org/10.1007/978-1-4614-6170-8_22
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-6169-2
Online ISBN: 978-1-4614-6170-8
eBook Packages: Computer ScienceReference Module Computer Science and Engineering