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A smartphone-based online social network trust evaluation system

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Abstract

The number of smartphone users has increased significantly over the last decade. The number of people using social networking sites is also increasing, and these platforms offer many features through which individuals can communicate with their contacts. The digital sphere is an opportunity for communication, but it is also an unprecedented arena for malicious attacks. The high quantity of personal and/or sensitive data, coupled with the large number of users, is one of the main motivations of malicious actors. We introduce in this paper a novel trust indicator for evaluating the contacts of an online social network user. This analysis is particularly important since the security policy of online social networks rests on the principle that a user’s contact is a person of trust. This assumption, not always verified as true, gives any number of people access to personal information. To address this problem, we propose applying a multi-layer model and extend it by proposing overlapping features that highlight the level of overlap of a contact belonging to the set of social networking friends of a smartphone user. We prove the efficiency of these features in evaluating trust using a case study with Facebook and Twitter.

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References

  • Abu-Nimeh S, Chen TM, Alzubi O (2011) Malicious and spam posts in online social networks. IEEE Comput 44(9):23–28

    Article  Google Scholar 

  • Acquist A, Carrara E, Stutzman F, Callas J, Schimmer K, Nadjm M, Gorge M, Ellison N, King P, Gross R, Golder S (2007) Security issues and recommendations for online social networks, October 2007

  • Adamic LA, Adar E (2001) Friends and neighbors on the web. Social Netw 25:211–230

    Article  Google Scholar 

  • Bader M, Baggili I (2010) iPhone 3GS Forensics: Logical analysis using Apple iTunes Backup Utility. Small Scale Digit Device Forensics J 4(1):1–15

  • Bastian M, Heymann S, Jacomy M (2009) Gephi: An Open source software for exploring and manipulating networks. In: In international AAAI conference on weblogs and social media (AAAI)

  • Benevenuto F, Magno G, Rodrigues T, Almeida V (2010) Detecting spammers on Twitter. In: Proceedings of the 7th annual collaboration, electronic messaging, anti-abuse and spam conference (CEAS)

  • Bergadano F, Gunetti D, Picardi C (2002) User authentication through keystroke dynamics. ACM Trans Inform Syst Secur 5(4):367–397

    Article  Google Scholar 

  • Bojars U, Passant A, Cyganiak R, Breslin J (2008) Weaving SIOC into the web of linked data. In: Proceedings of the WWW 2008 workshop linked data on the web (LDOW), Beijing, China

  • Boshmaf Y, Muslukhov I, Beznosov K, Ripeanu M (2011) The socialbot network: When Bots Socialize for Fame and Money. In: 27th annual computer security applications conference (ACSAC). ACM Press, New York, USA, p 93

  • Boyd D, Ellison NB (2007) Social network sites: definition, history, and scholarship. J Comput Mediat Commun 13(1–2):210–230

    Google Scholar 

  • Brickley D, Miller L (2007) The Friend of aFriend (FOAF) Vocabulary specification. Technical report

  • Caci B, Cardaci M, Tabacchi M (2012) Facebook as a small world: a topological hypothesis. Soc Netw Anal Min 2(2):163–167

    Article  Google Scholar 

  • Catanese S, Ferrara E, Fiumara G (2013) Forensic analysis of phone call networks. Soc Netw Anal Min 3(1):15–33

    Article  Google Scholar 

  • Chen K-T, Hong L-W (2007) User Identification based on game-play activity patterns. In: Proceedings of the 6th ACM SIGCOMM workshop on network and system support for games (SIGCOMM), pp 7–12. ACM

  • Christen P (2006) A comparison of personal name matching: techniques and practical issues. In: workshop on mining complex data (MCD), held at IEEE ICDM’06, Hong Kong, pp 290–294

  • Damerau FJ (1964) A technique for computer detection and correction of spelling errors. Commun ACM 7(3):171–176

    Article  Google Scholar 

  • Dellutri F, Laura L, Ottaviani V, Italiano GF (2009) Extracting social networks from seized smartphones and web data. In: Information forensics and security, 2009. (WIFS), pp 101–105

  • Ding L, Zhou L, Finin TW, Joshi A (2005) How the semantic web is being used: an analysis of FOAF documents. In: Hawaii international conference on system sciences (HICSS)

  • Elmagarmid AK, Ipeirotis PG, Verykios VS (2007) Duplicate record detection: a survey. IEEE Trans Knowl Data Eng 19(1):1–16

    Article  Google Scholar 

  • Facebook (2013a) Adding friends & friend requests, Sept 2013a. https://www.facebook.com/help/www/360212094049906

  • Facebook (2013b) People you may know, Sep 2013b. https://www.facebook.com/help/www/501283333222485/

  • Fette I, Sadeh N, Tomasic A (2007) Learning to detect phishing emails. In: Proceedings of the 16th international conference on World Wide Web (WWW). ACM, New York, NY, USA, pp 649–656

  • Gayo Avello D (2011) All liaisons are dangerous when all your friends are known to us. In: Proceedings of the 22nd ACM conference on Hypertext and hypermedia (HT). ACM, New York, NY, USA, pp 171–180

  • Ghosh S, Viswanath B, Kooti F, Sharma N, Korlam G, Benevenuto F, Ganguly N, Gummadi KP (2012) Understanding and combating link farming in the twitter social network. In: Proceedings of the 21st international conference on World Wide Web (WWW)

  • Girard A, Fallery B (2009) Digital social networks: literature review and research perspectives. In: Association Information and Management, June 2009

  • Golbeck J (2006) Trust on the world wide web: a survey. Found Trends Web Sci 1(2):131–197

    Google Scholar 

  • Gunes I, Kaleli C, Bilge A, Polat H (2012) Shilling attacks against recommender systems: a comprehensive survey. Artif Intell Rev 1–33. doi:10.1007/s10462-012-9364-9

  • Hamdi S, Lopes Gancarski A, Bouzeghoub A, Yahia SB (2012) IRIS: A novel method of direct trust computation for generating trusted social networks. In: IEEE 11th international conference on trust, security and privacy in computing and communications (TrustCom), pp 616–623

  • Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating (ROC) curvel characteristic. Radiology 143(1):29–36

    Google Scholar 

  • Hossmann T, Legendre F, Nomikos G, Spyropoulos T (2009) Stumbl: using facebook to collect rich datasets for opportunistic networking research. In: IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks

  • Jaro MA (1989) Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida. J Am Stat Assoc

  • Jiang W, Wang G (2011) SWTrust: generating trusted graph for trust evaluation in online social networks. In: IEEE 10th international conference on trust, security and privacy in computing and communications (TrustCom), 2011, pp 320–327

  • Kim M, Seo J, Noh S, Han S (2012) Identity management-based social trust model for mediating information sharing and privacy enhancement. Secur Commun Netw 5(8):887–897

    Article  Google Scholar 

  • Kleinberg J (2000) The small-world phenomenon: an algorithm perspective. In: Proceedings of the thirty-second annual ACM symposium on theory of computing. ACM, New York, NY, USA, pp 163–170

  • Korovaiko N, Thomo A (2013) Trust prediction from user-item ratings. Soc Netw Anal Min 3(3):749–759

    Article  Google Scholar 

  • Kreibich JA (2010) Using SQLite. O’Reilly Media, 1st edn. California: O'Reilly Media, California

  • Kukich K (1992) Techniques for automatically correcting words in text. ACM Comput Surv 24(4):377–439

    Article  Google Scholar 

  • Lee K, Caverlee J, Webb S (2010) The social honeypot project. In: Proceedings of the 19th international conference on World wide web (WWW). ACM Press, New York, New York, USA, p 1139

  • Lee R, Sumiya K (2010) Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection. In: Proceedings of the 2nd ACM SIGSPATIAL iInternational workshop on location based social networks (SIGSPATIAL). ACM, New York, NY, USA, pp 1–10

  • Leicht EA, Holme P, Newman MEJ (2006) Vertex similarity in networks. Phys Rev E 73(2):026120

    Article  Google Scholar 

  • Levenshtein V (1966) Binary codes capable of correcting deletions, insertions, and reversals. Sov phys Doklady10(8):707–710

    MathSciNet  Google Scholar 

  • Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inform Sci Technol 58:1019–1031

    Article  Google Scholar 

  • Lü L, Zhou T (2011) Link prediction in complex networks: a survey. Phys A Stat Mech Appl 390(6):1150–1170

    Article  Google Scholar 

  • Magnani M, Rossi L (2011) The ML-model for multi-layer social networks. In: Advances in social networks analysis and mining (ASONAM). IEEE Computer Society, pp 5–12

  • Massa P, Avesani P (2007) Trust metrics on controversial users. Int J Semant Web Inform Syst 3(1):39–64

    Article  Google Scholar 

  • Melnikov N, Schönwälder J (2010) Cybermetrics: user identification through network flow analysis. In: Proceedings of the mechanisms for autonomous management of networks and services, and 4th international conference on Autonomous infrastructure, management and security (AIMS). Springer, pp 167–170.

  • Mika P (2005) Flink: Semantic Web technology for the extraction and analysis of social networks. Web Seman Sci Serv Agents World Wide Web 3(2-3):211–223

    Article  Google Scholar 

  • Molloy M, Reed B (1995) A critical point for random graphs with a given degree sequence. Random Struct Algorithms 6(2-3):161–180

    Article  MathSciNet  MATH  Google Scholar 

  • Nagle F, Singh L (2009) Can friends be trusted? Exploring privacy in online social networks. In: International conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 312–315

  • Nepal S, Sherchan W, Paris C (2011) STrust: a trust model for social networks. In: 2011 IEEE 10th international conference on trust, security and privacy in computing and communications (TrustCom), pp 841–846

  • Newman C (2004) SQLite (Developer’s Library). Sams, Indianapolis, USA

  • Perez C, Birregah B, Lemercier M (2012) The Multi-layer imbrication for data leakage prevention from mobile devices. In: 2012 IEEE 11th International Conference on trust, security and privacy in computing and communications (TrustCom), pp 813–819. IEEE

  • Perez C, Lemercier M, Birregah B, Corpel A (2011) SPOT 1.0: Scoring suspicious profiles on twitter. In: 2011 international conference on advances in social networks analysis and mining (ASONAM), pp 377–381. IEEE

  • Porter EH, Winkler WE (1997) Approximate string comparison and its effect on an advanced record linkage system. In: Advanced record linkage system. U.S. Bureau of the Census, Research Report, pp 190–199

  • Raad E, Chbeir R, Dipanda A (2010) User profile matching in social networks. In: 13th international conference on network-based information systems (NBiS), pp 297–304. IEEE

  • Ravasz E, Barabási A (2003) Hierarchical organization in complex networks. Phys Rev E 67(2):026112

    Article  Google Scholar 

  • Rowe M, Ciravegna F (2008) Disambiguating identity through social circles and social data. In: 1st international workshop on collective semantics: collective intelligence & the semantic web (CISWeb)

  • Shi L, Berrueta D, Fern+ndez S, Polo L Fernandez S (2008) Smushing rdf instances: are alice and bob the same open source developer? In: ISWC2008 workshop on personal identification and collaborations: knowledge mediation and extraction (PICKME), October

  • Singla P, Domingos P (2005) Object identification with attribute-mediated dependences. In: Proceedings of 9th European conference on principles and practice of knowledge discovery in databases (PKDD), pp 297–308

  • Stringhini G, Kruegel C, Vigna G (2012) Detecting spammers on social networks. In: Proceedings of the 26th annual computer security applications conference (ACSAC), pp 1–9. ACM Request Permissions

  • Tchuente D, Canut M, Jessel N, Peninou A, Sedes F (2013) A community-based algorithm for deriving users’ profiles from egocentrics networks: experiment on facebook and dblp. Soc Netw Anal Min 3(3):667–683

    Article  Google Scholar 

  • Vorvoreanu M (2010) Managing identity across social networks. In: Poster session at the 2010 conference on computer supported cooperative work

  • Wang AH (2010a) Detecting spam bots in online social networking sites: a machine learning approach. In: Proceedings of the 24th annual IFIP WG 11.3 working conference on data and applications security and privacy (DBSec), pp 335–342. Springer, Berlin, Heidelberg

  • Wang AH (2010b) Don’t follow me: Spam detection in twitter. In: Conference on security and cryptography (SECRYPT)

  • Yancey WE (2005) Evaluating string comparator performance for record linkage. U.S. Bureau of the Census, Research Report

  • Zhou T, Lü L, Zhang YC (2009) Predicting missing links via local information. Eur Phys J B 71(4):623–630

    Google Scholar 

  • Zobel J, Dart P (1996) Phonetic string matching: lessons from information retrieval. In: Proceedings of the 19th annual international ACM SIGIR conference on research and development in information retrieval (SIGIR). ACM, New York, NY, USA, pp 166–172

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Acknowledgment

This work is part of the CyNIC (Cybercrime, Nomadism and IntelligenCe) CPER project supported by the Champagne-Ardenne region and European Regional Development Fund (ERDF).

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Correspondence to Charles Perez.

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Perez, C., Birregah, B. & Lemercier, M. A smartphone-based online social network trust evaluation system. Soc. Netw. Anal. Min. 3, 1293–1310 (2013). https://doi.org/10.1007/s13278-013-0138-4

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  • DOI: https://doi.org/10.1007/s13278-013-0138-4

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