Advertisement

Network-aware privacy risk estimation in online social networks

  • Ruggero G. PensaEmail author
  • Gianpiero Di Blasi
  • Livio Bioglio
Original Article
  • 9 Downloads

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.

Keywords

Privacy measures Online social networks Centrality Simulation Computational social science 

Notes

Acknowledgements

This work is supported by Fondazione CRT (Grant Numbers 2015-1638 and 2017-2323). The authors wish to thank the anonymous reviewers for their valuable comments and all the volunteers who participated in the survey.

Supplementary material

13278_2019_558_MOESM1_ESM.csv (15.5 mb)
Supplementary material 1 (csv 15872 KB)
13278_2019_558_MOESM2_ESM.csv (8 kb)
Supplementary material 2 (csv 7 KB)

References

  1. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723MathSciNetCrossRefGoogle Scholar
  2. 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 9–19Google Scholar
  3. Akcora CG, Carminati B, Ferrari E (2012b) Risks of friendships on social networks. In: Proceedings of IEEE ICDM 2012. IEEE Computer Society, pp 810–815Google Scholar
  4. Becker J, Chen H (2009) Measuring privacy risk in online social networks. In: Proceedings of web 2.0 security and privacy (W2SP) 2009Google Scholar
  5. 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):289–300MathSciNetzbMATHGoogle Scholar
  6. Bianchini M, Gori M, Scarselli F (2005) Inside pagerank. ACM Trans Internet Technol 5(1):92–128CrossRefGoogle Scholar
  7. 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 28–37CrossRefGoogle Scholar
  8. Bioglio L, Capecchi S, Peiretti F, Sayed D, Torasso A, Pensa RG (2018) A social network simulation game to raise awareness of privacy among school children. IEEE Trans Learn Technol.  https://doi.org/10.1109/TLT.2018.2881193 CrossRefGoogle Scholar
  9. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw 30(1–7):107–117Google Scholar
  10. Buccafurri F, Fotia L, Lax G, Saraswat V (2016) Analysis-preserving protection of user privacy against information leakage of social-network likes. Inf Sci 328:340–358CrossRefGoogle Scholar
  11. Cavoukian A (2012) Privacy by design [leading edge]. IEEE Technol Soc Mag 31(4):18–19CrossRefGoogle Scholar
  12. 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 2014Google Scholar
  13. Chen Y, Gan Q, Suel T (2004) Local methods for estimating pagerank values. In: Proceedings of ACM CIKM 2004. ACM, pp 381–389Google Scholar
  14. Choi H, Park J, Jung Y (2018) The role of privacy fatigue in online privacy behavior. Comput Hum Behav 81:42–51CrossRefGoogle Scholar
  15. Cormode G, Srivastava D, Bhagat S, Krishnamurthy B (2009) Class-based graph anonymization for social network data. PVLDB 2(1):766–777Google Scholar
  16. 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 YorkGoogle Scholar
  17. Delignette-Muller ML, Dutang C (2015) fitdistrplus: an R package for fitting distributions. J Stat Softw 64(4):1–34CrossRefGoogle Scholar
  18. 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):150292MathSciNetCrossRefGoogle Scholar
  19. 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 619–630Google Scholar
  20. Fang L, LeFevre K (2010) Privacy wizards for social networking sites. In: Proceedings of WWW 2010. ACM, pp 351–360Google Scholar
  21. Furini M, Tamanini V (2015) Location privacy and public metadata in social media platforms: attitudes, behaviors and opinions. Multimed Tools Appl 74(21):9795–9825CrossRefGoogle Scholar
  22. Golub GH, van der Vorst HA (2000) Eigenvalue computation in the 20th century. J Comput Appl Math 123(1–2):35–65MathSciNetCrossRefGoogle Scholar
  23. González RJ (2017) Hacking the citizenry?: personality profiling, “big data” and the election of donald trump. Anthropol Today 33(3):9–12CrossRefGoogle Scholar
  24. Gruhl D, Liben-Nowell D, Guha RV, Tomkins A (2004) Information diffusion through blogspace. SIGKDD Explor 6(2):43–52CrossRefGoogle Scholar
  25. Hay M, Miklau G, Jensen D, Towsley DF, Weis P (2008) Resisting structural re-identification in anonymized social networks. PVLDB 1(1):102–114Google Scholar
  26. 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 169–178Google Scholar
  27. Jeh G, Widom J (2003) Scaling personalized web search. In: Proceedings of WWW 2003. ACM, pp 271–279Google Scholar
  28. Kamvar SD, Haveliwala TH, Manning CD, Golub GH (2003) Extrapolation methods for accelerating pagerank computations. In: Proceedings of WWW 2003. ACM, pp 261–270Google Scholar
  29. 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):98–100CrossRefGoogle Scholar
  30. Kosinski M, Stillwell D, Graepel T (2013) Private traits and attributes are predictable from digital records of human behavior. PNAS 110(15):5802–5805CrossRefGoogle Scholar
  31. Litt E (2013) Understanding social network site users’ privacy tool use. Comput Hum Behav 29(4):1649–1656CrossRefGoogle Scholar
  32. Liu K, Terzi E (2008) Towards identity anonymization on graphs. In: Proceedings of ACM SIGMOD 2008. ACM, pp 93–106Google Scholar
  33. Liu K, Terzi E (2010) A framework for computing the privacy scores of users in online social networks. TKDD 5(1):6:1–6:30CrossRefGoogle Scholar
  34. 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 61–70Google Scholar
  35. McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Annu Rev Sociol 27(1):415–444CrossRefGoogle Scholar
  36. McSherry F (2005) A uniform approach to accelerated pagerank computation. In: Proceedings of WWW 2005. ACM, pp 575–582Google Scholar
  37. Misra G, Such JM (2016) How socially aware are social media privacy controls? IEEE Comput 49(3):96–99CrossRefGoogle Scholar
  38. Newman M (2010) Networks: an introduction. Oxford University Press Inc., New York, NYCrossRefGoogle Scholar
  39. Pensa RG, Blasi GD (2017) A privacy self-assessment framework for online social networks. Expert Syst Appl 86:18–31CrossRefGoogle Scholar
  40. Rathore S, Sharma PK, Loia V, Jeong YS, Park JH (2017) Social network security: issues, challenges, threats, and solutions. Inf Sci 421:43–69CrossRefGoogle Scholar
  41. Roberts SGB, Dunbar RIM, Pollet TV, Kuppens T (2009) Exploring variation in active network size: constraints and ego characteristics. Soc Netw 31(2):138–146CrossRefGoogle Scholar
  42. 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 295–304Google Scholar
  43. Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15(1):72–101CrossRefGoogle Scholar
  44. Squicciarini AC, Paci F, Sundareswaran S (2014) Prima: a comprehensive approach to privacy protection in social network sites. Ann Télécommun 69(1–2):21–36CrossRefGoogle Scholar
  45. Such JM, Criado N (2016) Resolving multi-party privacy conflicts in social media. IEEE Trans Knowl Data Eng 28(7):1851–1863CrossRefGoogle Scholar
  46. Such JM, Rovatsos M (2016) Privacy policy negotiation in social media. TAAS 11(1):4:1–4:29CrossRefGoogle Scholar
  47. Talukder N, Ouzzani M, Elmagarmid AK, Elmeleegy H, Yakout M (2010) Privometer: privacy protection in social networks. In: Proceedings of M3SN’10. IEEE, pp 266–269Google Scholar
  48. Task C, Clifton C (2012) A guide to differential privacy theory in social network analysis. In: Proceedings of ASONAM 2012. IEEE, pp 411–417Google Scholar
  49. Ugander J, Karrer B, Backstrom L, Marlow C (2011) The anatomy of the Facebook social graph. CoRR arXiv:abs/1111.4503
  50. Vuokko N, Terzi E (2010) Reconstructing randomized social networks. In: Proceedings of SIAM SDM 2010. SIAM, pp 49–59Google Scholar
  51. Wagner I, Eckhoff D (2018) Technical privacy metrics: a systematic survey. ACM Comput Surv 51(3):57:1–57:38CrossRefGoogle Scholar
  52. Wang Y, Nepali RK, Nikolai J (2014) Social network privacy measurement and simulation. In: Proceedings of ICNC 2014. IEEE, pp 802–806Google Scholar
  53. 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):199–210CrossRefGoogle Scholar
  54. Ying X, Wu X (2011) On link privacy in randomizing social networks. Knowl Inf Syst 28(3):645–663CrossRefGoogle Scholar
  55. 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):1005–1016CrossRefGoogle Scholar
  56. Zheleva E, Getoor L (2011) Privacy in social networks: a survey. In: Aggarwal C (ed) Social network data analytics. Springer, Boston, MA, pp 277–306CrossRefGoogle Scholar
  57. 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):47–77CrossRefGoogle Scholar
  58. Zou L, Chen L, Özsu MT (2009) K-automorphism: a general framework for privacy preserving network publication. PVLDB 2(1):946–957Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of TurinTurinItaly

Personalised recommendations