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Social Network Analysis and Mining
Published in: Social Network Analysis and Mining 1/2019

01-12-2019 | Original Article

Network-aware privacy risk estimation in online social networks

Authors: Ruggero G. Pensa, Gianpiero Di Blasi, Livio Bioglio

Published in: Social Network Analysis and Mining | Issue 1/2019

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Abstract

Online social networks expose their users to privacy leakage risks. To measure the risk, privacy scores can be computed to quantify the users’ profile exposure according to their privacy preferences or attitude. However, user privacy can be also influenced by external factors (e.g., the relative risk of the network, the position of the user within the social graph), but state-of-the-art scores do not consider such properties adequately. We define a network-aware privacy score that improves the measurement of user privacy risk according to the characteristics of the network. We assume that users that lie in an unsafe portion of the network are more at risk than users that are mostly surrounded by privacy-aware friends. The effectiveness of our measure is analyzed by means of extensive experiments on two simulated networks and a large graph of real social network users.
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Appendix
Available only for authorised users
Footnotes
2
In this work, we refer to T as a fixed set of user-decided topics/aspects. It is out of the scope of this paper to consider automatic topic/aspect inference of each user’s action or posted item.
 
6
The data collection/storage and processing protocols have been approved by the Law Office of our institution.
 
7
The typical shape of the Gamma distribution is a skewed bell.
 
Literature
Akcora CG, Carminati B, Ferrari E (2012a) Privacy in social networks: how risky is your social graph? In: Proceedings of IEEE ICDE 2012. IEEE Computer Society, pp 9–19
Akcora CG, Carminati B, Ferrari E (2012b) Risks of friendships on social networks. In: Proceedings of IEEE ICDM 2012. IEEE Computer Society, pp 810–815
Becker J, Chen H (2009) Measuring privacy risk in online social networks. In: Proceedings of web 2.0 security and privacy (W2SP) 2009
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol) 57(1):289–300MathSciNetMATH
Bianchini M, Gori M, Scarselli F (2005) Inside pagerank. ACM Trans Internet Technol 5(1):92–128CrossRef
Bioglio L, Pensa RG (2017) Impact of neighbors on the privacy of individuals in online social networks. In: Proceedings of the international conference on computational science, ICCS 2017, 12–14 June 2017, Procedia Computer Science, vol 108. Elsevier, Zurich, Switzerland, pp 28–37
Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw 30(1–7):107–117
Buccafurri F, Fotia L, Lax G, Saraswat V (2016) Analysis-preserving protection of user privacy against information leakage of social-network likes. Inf Sci 328:340–358CrossRef
Cavoukian A (2012) Privacy by design [leading edge]. IEEE Technol Soc Mag 31(4):18–19CrossRef
Cetto A, Netter M, Pernul G, Richthammer C, Riesner M, Roth C, Sänger J (2014) Friend inspector: a serious game to enhance privacy awareness in social networks. In: Proceedings of IDGEI 2014
Chen Y, Gan Q, Suel T (2004) Local methods for estimating pagerank values. In: Proceedings of ACM CIKM 2004. ACM, pp 381–389
Choi H, Park J, Jung Y (2018) The role of privacy fatigue in online privacy behavior. Comput Hum Behav 81:42–51CrossRef
Cormode G, Srivastava D, Bhagat S, Krishnamurthy B (2009) Class-based graph anonymization for social network data. PVLDB 2(1):766–777
Cullen AC, Frey HC (1999) Probabilistic techniques in exposure assessment: a handbook for dealing with variability and uncertainty in models and inputs. Plenum Press, New York
Delignette-Muller ML, Dutang C (2015) fitdistrplus: an R package for fitting distributions. J Stat Softw 64(4):1–34CrossRef
Dunbar RIM (2016) Do online social media cut through the constraints that limit the size of offline social networks? R Soc Open Sci 3(1):150292MathSciNetCrossRef
Erling O, Averbuch A, Larriba-Pey J, Chafi H, Gubichev A, Prat-Pérez A, Pham M, Boncz PA (2015) The LDBC social network benchmark: interactive workload. In: Proceedings of ACM SIGMOD 2015. ACM, pp 619–630
Fang L, LeFevre K (2010) Privacy wizards for social networking sites. In: Proceedings of WWW 2010. ACM, pp 351–360
Furini M, Tamanini V (2015) Location privacy and public metadata in social media platforms: attitudes, behaviors and opinions. Multimed Tools Appl 74(21):9795–9825CrossRef
Golub GH, van der Vorst HA (2000) Eigenvalue computation in the 20th century. J Comput Appl Math 123(1–2):35–65MathSciNetCrossRef
González RJ (2017) Hacking the citizenry?: personality profiling, “big data” and the election of donald trump. Anthropol Today 33(3):9–12CrossRef
Gruhl D, Liben-Nowell D, Guha RV, Tomkins A (2004) Information diffusion through blogspace. SIGKDD Explor 6(2):43–52CrossRef
Hay M, Miklau G, Jensen D, Towsley DF, Weis P (2008) Resisting structural re-identification in anonymized social networks. PVLDB 1(1):102–114
Hay M, Li C, Miklau G, Jensen D (2009) Accurate estimation of the degree distribution of private networks. In: Proceedings of ICDM 2009. IEEE, pp 169–178
Jeh G, Widom J (2003) Scaling personalized web search. In: Proceedings of WWW 2003. ACM, pp 271–279
Kamvar SD, Haveliwala TH, Manning CD, Golub GH (2003) Extrapolation methods for accelerating pagerank computations. In: Proceedings of WWW 2003. ACM, pp 261–270
Keller LA, Schweid JA (2011) Handbook of polytomous item response theory models edited by Michael L. Nering and Remo Ostini. J Educ Meas 48(1):98–100CrossRef
Kosinski M, Stillwell D, Graepel T (2013) Private traits and attributes are predictable from digital records of human behavior. PNAS 110(15):5802–5805CrossRef
Litt E (2013) Understanding social network site users’ privacy tool use. Comput Hum Behav 29(4):1649–1656CrossRef
Liu K, Terzi E (2008) Towards identity anonymization on graphs. In: Proceedings of ACM SIGMOD 2008. ACM, pp 93–106
Liu K, Terzi E (2010) A framework for computing the privacy scores of users in online social networks. TKDD 5(1):6:1–6:30CrossRef
Liu Y, Gummadi PK, Krishnamurthy B, Mislove A (2011) Analyzing Facebook privacy settings: user expectations vs. reality. In: Proceedings of ACM SIGCOMM IMC ’11. ACM, pp 61–70
McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Annu Rev Sociol 27(1):415–444CrossRef
McSherry F (2005) A uniform approach to accelerated pagerank computation. In: Proceedings of WWW 2005. ACM, pp 575–582
Misra G, Such JM (2016) How socially aware are social media privacy controls? IEEE Comput 49(3):96–99CrossRef
Newman M (2010) Networks: an introduction. Oxford University Press Inc., New York, NYCrossRef
Pensa RG, Blasi GD (2017) A privacy self-assessment framework for online social networks. Expert Syst Appl 86:18–31CrossRef
Rathore S, Sharma PK, Loia V, Jeong YS, Park JH (2017) Social network security: issues, challenges, threats, and solutions. Inf Sci 421:43–69CrossRef
Roberts SGB, Dunbar RIM, Pollet TV, Kuppens T (2009) Exploring variation in active network size: constraints and ego characteristics. Soc Netw 31(2):138–146CrossRef
Song X, Wang X, Nie L, He X, Chen Z, Liu W (2018) A personal privacy preserving framework: I let you know who can see what. In: Proceedings of ACM SIGIR 2018, 08–12 July. ACM, Ann Arbor, MI, pp 295–304
Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15(1):72–101CrossRef
Squicciarini AC, Paci F, Sundareswaran S (2014) Prima: a comprehensive approach to privacy protection in social network sites. Ann Télécommun 69(1–2):21–36CrossRef
Such JM, Criado N (2016) Resolving multi-party privacy conflicts in social media. IEEE Trans Knowl Data Eng 28(7):1851–1863CrossRef
Such JM, Rovatsos M (2016) Privacy policy negotiation in social media. TAAS 11(1):4:1–4:29CrossRef
Talukder N, Ouzzani M, Elmagarmid AK, Elmeleegy H, Yakout M (2010) Privometer: privacy protection in social networks. In: Proceedings of M3SN’10. IEEE, pp 266–269
Task C, Clifton C (2012) A guide to differential privacy theory in social network analysis. In: Proceedings of ASONAM 2012. IEEE, pp 411–417
Vuokko N, Terzi E (2010) Reconstructing randomized social networks. In: Proceedings of SIAM SDM 2010. SIAM, pp 49–59
Wagner I, Eckhoff D (2018) Technical privacy metrics: a systematic survey. ACM Comput Surv 51(3):57:1–57:38CrossRef
Wang Y, Nepali RK, Nikolai J (2014) Social network privacy measurement and simulation. In: Proceedings of ICNC 2014. IEEE, pp 802–806
Xu K, Guo Y, Guo L, Fang Y, Li X (2017) My privacy my decision: control of photo sharing on online social networks. IEEE Trans Dependable Secur Comput 14(2):199–210CrossRef
Ying X, Wu X (2011) On link privacy in randomizing social networks. Knowl Inf Syst 28(3):645–663CrossRef
Yu J, Zhang B, Kuang Z, Lin D, Fan J (2017) iprivacy: image privacy protection by identifying sensitive objects via deep multi-task learning. IEEE Trans Inf Forensics Secur 12(5):1005–1016CrossRef
Zheleva E, Getoor L (2011) Privacy in social networks: a survey. In: Aggarwal C (ed) Social network data analytics. Springer, Boston, MA, pp 277–306CrossRef
Zhou B, Pei J (2011) The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks. Knowl Inf Syst 28(1):47–77CrossRef
Zou L, Chen L, Özsu MT (2009) K-automorphism: a general framework for privacy preserving network publication. PVLDB 2(1):946–957
Metadata
Title
Network-aware privacy risk estimation in online social networks
Authors
Ruggero G. Pensa
Gianpiero Di Blasi
Livio Bioglio
Publication date
01-12-2019
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2019
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-019-0558-x

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