Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Risk-Based Privacy-Aware Access Control for Threat Detection Systems

  • Chapter
  • First Online:
Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVI

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 10720))

Abstract

Threat detection systems collect and analyze a large amount of security data logs for detecting potential attacks. Since log data from enterprise systems may contain sensitive and personal information access should be limited to the data relevant to the task at hand as mandated by data protection regulations. To this end, data need to be pre-processed (anonymized) to eliminate or obfuscate the sensitive information that is not-strictly necessary for the task. Additional security/accountability measures may be also applied to reduce the privacy risk, such as logging the access to the personal data or imposing deletion obligations. Anonymization reduces the privacy risk, but it should be carefully applied and balanced with utility requirements of the different phases of the process: a preliminary analysis may require fewer details than an in-depth investigation on a suspect set of logs. We propose a risk-based privacy-aware access control framework for threat detection systems, where each access request is evaluated by comparing the privacy-risk and the trustworthiness of the request. When the risk is too large compared to the trust level, the framework can apply adaptive adjustment strategies to decrease the risk (e.g., by selectively obfuscating the data) or to increase the trust level to perform a given task (e.g., imposing enforceable obligations to the user). We show how the framework can simultaneously address both the privacy and the utility requirements. The experimental results presented in the paper that the framework leads to meaningful results, and real-time performance, within an industrial threat detection solution.

Parts of this paper have been presented at the International Conference on Future Data and Security Engineering [28]. This paper extends [28] in a number of ways: the theoretical framework has been extended so to include, in particular, explicit deny to data access (cf. Sect. 3.1); the adjustment decision from the authorization standpoint and its effect on the resulting response is now described; a new section (Sect. 4) presenting an architecture for proposed risk-based and privacy-aware access control framework and the data request evaluation workflow using the use case as running example has been added; a sketch of the policies to be used to express the risk-based authorizations (Sect. 5) is given; finally, both the Introduction and Conclusions have been improved.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We refer to these systems as TDS, to distinguish them from network-level intrusion detection systems (often called IDS or SIEM). We base our description on the SAP Enterprise Threat Detection, but the analysis can be applied to other solutions, including IDS. For a comparison between application- and network-level intrusion detection systems, see [22].

  2. 2.

    Other privacy metrics exist (for example, \(\ell \)-diversity, and t-closeness, see [19] for a survey), but k-anonymity is still a de-facto standard in real-world applications.

  3. 3.

    For a discussion on how relating this definition with more classical trust metrics, see [3]. See [21] for a survey of different approaches to defining trust.

  4. 4.

    In the XACML3.0 (eXtensible Access Control Markup Language) standard [17] the PDP is the point that evaluates an access request against an authorization policy and issues an access decision and the PEP Policy Enforcement Point is the point that intercepts user’s request call the PDP for an access decision then enforce this decision by allowing or denying the access. The PIP is the point that can be called to provide additional information about the resource, requester or environment.

  5. 5.

    For an analysis of the performance of the model on a benchmark dataset see [4].

References

  1. Ali, M., Bussard, L., Pinsdorf, U.: Obligation language for access control and privacy policies (2010)

    Google Scholar 

  2. Ardagna, C.A., Cremonini, M., De Capitani di Vimercati, S., Samarati, P.: A privacy-aware access control system. J. Comput. Secur. 16(4), 369–397 (2008)

    Article  Google Scholar 

  3. Armando, A., Bezzi, M., Cerbo, F., Metoui, N.: Balancing trust and risk in access control. In: Debruyne, C., Panetto, H., Meersman, R., Dillon, T., Weichhart, G., An, Y., Ardagna, C.A. (eds.) OTM 2015. LNCS, vol. 9415, pp. 660–676. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26148-5_45

    Chapter  Google Scholar 

  4. Armando, A., Bezzi, M., Metoui, N., Sabetta, A.: Risk-aware information disclosure. In: Garcia-Alfaro, J., Herrera-Joancomartí, J., Lupu, E., Posegga, J., Aldini, A., Martinelli, F., Suri, N. (eds.) DPM/QASA/SETOP 2014. LNCS, vol. 8872, pp. 266–276. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-17016-9_17

    Google Scholar 

  5. Armando, A., Bezzi, M., Metoui, N., Sabetta, A.: Risk-based privacy-aware information disclosure. Int. J. Secur. Softw. Eng. 6(2), 70–89 (2015)

    Article  Google Scholar 

  6. Baracaldo, N., Joshi, J.: Beyond accountability: using obligations to reduce risk exposure and deter insider attacks. In: Proceedings of the 18th ACM Symposium on Access Control Models and Technologies, SACMAT 2013, pp. 213–224. ACM, New York (2013)

    Google Scholar 

  7. Bettini, C., Jajodia, S., Wang, X.S., Wijesekera, D.: Provisions and obligations in policy management and security applications. In: Proceedings of the 28th International Conference on Very Large Data Bases, VLDB 2002, pp. 502–513. VLDB Endowment (2002)

    Google Scholar 

  8. Bezzi, M.: An information theoretic approach for privacy metrics. Trans. Data Priv. 3(3), 199–215 (2010)

    MathSciNet  Google Scholar 

  9. Brickell, J., Shmatikov, V.: The cost of privacy: destruction of data-mining utility in anonymized data publishing. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 70–78. ACM, New York (2008)

    Google Scholar 

  10. Chen, L., Crampton, J.: Risk-aware role-based access control. In: Meadows, C., Fernandez-Gago, C. (eds.) STM 2011. LNCS, vol. 7170, pp. 140–156. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29963-6_11

    Chapter  Google Scholar 

  11. Chen, L., Crampton, J., Kollingbaum, M.J., Norman, T.J.: Obligations in risk-aware access control. In: Cuppens-Boulahia, N., Fong, P., García-Alfaro, J., Marsh, S., Steghöfer, J. (eds.) PST, pp. 145–152. IEEE (2012)

    Google Scholar 

  12. Cheng, P.-C., Rohatgi, P., Keser, C., Karger, P.A., Wagner, G.M., Reninger, A.S.: Fuzzy multi-level security: an experiment on quantified risk-adaptive access control. In: IEEE Symposium on Security and Privacy, pp. 222–230. IEEE Computer Society (2007)

    Google Scholar 

  13. Ciriani, V., De Capitani di Vimercati, S., Foresti, S., Samarati, P.: Theory of privacy and anonymity. In: Atallah, M., Blanton, M. (eds.) Algorithms and Theory of Computation Handbook, 2nd edn. CRC Press, Boca Raton (2009)

    Google Scholar 

  14. Clifton, C., Tassa, T.: On syntactic anonymity and differential privacy. Trans. Data Priv. 6(2), 161–183 (2013)

    MathSciNet  Google Scholar 

  15. Di Cerbo, F., Doliere, F., Gomez, L., Trabelsi, S.: PPL v2.0: uniform data access and usage control on cloud and mobile. In: Proceedings of the 1st International Workshop on TEchnical and LEgal Aspects of Data PRIvacy and SEcurity. IEEE (2015)

    Google Scholar 

  16. Dickens, L., Russo, A., Cheng, P.-C., Lobo, J.: Towards learning risk estimation functions for access control. In: Snowbird Learning Workshop (2010)

    Google Scholar 

  17. eXtensible Access Control Markup Language (XACML) Version 3.0, January 2013. http://docs.oasis-open.org/xacml/3.0/xacml-3.0-core-spec-os-en.pdf

  18. Friedewald, M., Pohoryles, R.J.: Privacy and Security in the Digital Age: Privacy in the Age of Super-Technologies. Routledge, London (2016)

    Google Scholar 

  19. Fung, B.C.M., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing: a survey of recent developments. ACM Comput. Surv. 42(4), 4:1–4:153 (2010)

    Article  Google Scholar 

  20. Ghinita, G., Karras, P., Kalnis, P., Mamoulis, N.: Fast data anonymization with low information loss. In: Proceedings of the 33rd International Conference on Very Large Data Bases, pp. 758–769. VLDB Endowment (2007)

    Google Scholar 

  21. Josang, A., Ismail, R., Boyd, C.: A survey of trust and reputation systems for online service provision. Decis. Support Syst. 43(2), 618–644 (2007). Emerging Issues in Collaborative Commerce

    Article  Google Scholar 

  22. Kaempfer, M. (2015). http://scn.sap.com/community/security/blog/2015/03/04/sap-enterprise-threat-detection-and-siem-is-this-not-the-same

  23. Kohlmayer, F., Prasser, F., Eckert, C., Kuhn, K.A.: A flexible approach to distributed data anonymization. J. Biomed. Inform. 50, 62–76 (2014). Special Issue on Informatics Methods in Medical Privacy

    Article  Google Scholar 

  24. Kounine, A., Bezzi, M.: Assessing disclosure risk in anonymized datasets. In: Proceedings of the FloCon Workshop, January 2009

    Google Scholar 

  25. Lakkaraju, K., Slagell, A.: Evaluating the utility of anonymized network traces for intrusion detection. In: Proceedings of the 4th International Conference on Security and Privacy in Communication Netowrks, SecureComm 2008, pp. 17:1–17:8. ACM, New York (2008)

    Google Scholar 

  26. Li, X., Zhou, F., Yang, X.: A multi-dimensional trust evaluation model for large-scale P2P computing. J. Parallel Distrib. Comput. 71(6), 837–847 (2011)

    Article  MATH  Google Scholar 

  27. Metoui, N., Bezzi, M.: Differential privacy based access control. In: Debruyne, C., et al. (eds.) OTM 2016. LNCS, vol. 10033, pp. 962–974. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48472-3_61

    Chapter  Google Scholar 

  28. Metoui, N., Bezzi, M., Armando, A.: Trust and risk-based access control for privacy preserving threat detection systems. In: Dang, T.K., Wagner, R., Küng, J., Thoai, N., Takizawa, M., Neuhold, E. (eds.) FDSE 2016. LNCS, vol. 10018, pp. 285–304. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48057-2_20

    Chapter  Google Scholar 

  29. Mivule, K., Anderson, B.: A study of usability-aware network trace anonymization. In: Science and Information Conference (SAI), pp. 1293–1304. IEEE (2015)

    Google Scholar 

  30. Mont, M.C., Beato, F.: On parametric obligation policies: enabling privacy-aware information lifecycle management in enterprises. In: Eighth IEEE International Workshop on Policies for Distributed Systems and Networks, POLICY 2007, pp. 51–55. IEEE (2007)

    Google Scholar 

  31. Narayanan, A., Huey, J., Felten, E.W.: A precautionary approach to big data privacy. In: Gutwirth, S., Leenes, R., De Hert, P. (eds.) Data Protection on the Move, pp. 357–385. Springer, Dordrecht (2016). https://doi.org/10.1007/978-94-017-7376-8_13

    Chapter  Google Scholar 

  32. Council of Europe: Handbook on European data protection law. Technical report (2014)

    Google Scholar 

  33. Committee on Strategies for Responsible Sharing of Clinical Trial Data: Sharing Clinical Trial Data: Maximizing Benefits, Minimizing Risk. National Academies Press, Washington, DC (2015)

    Google Scholar 

  34. Oprea, A., Li, Z., Yen, T.-F., Chin, S.H., Alrwais, S.: Detection of early-stage enterprise infection by mining large-scale log data. In: 2015 45th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), pp. 45–56. IEEE (2015)

    Google Scholar 

  35. Pretschner, A., Hilty, M., Basin, D.: Distributed usage control. Commun. ACM 49(9), 39–44 (2006)

    Article  Google Scholar 

  36. Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)

    Article  Google Scholar 

  37. Sandhu, R., Park, J.: Usage control: a vision for next generation access control. In: Gorodetsky, V., Popyack, L., Skormin, V. (eds.) MMM-ACNS 2003. LNCS, vol. 2776, pp. 17–31. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45215-7_2

    Chapter  Google Scholar 

  38. Scholl, M.A., Stine, K.M., Hash, J., Bowen, P., Johnson, L.A., Smith, C.D., Steinberg, D.I.: SP 800–66 REV. 1. An introductory resource guide for implementing the health insurance portability and accountability act (HIPAA) security rule. Technical report (2008)

    Google Scholar 

  39. Shaikh, R.A., Adi, K., Logrippo, L.: Dynamic risk-based decision methods for access control systems. Comput. Secur. 31(4), 447–464 (2012)

    Article  Google Scholar 

  40. Templ, M., Meindl, B., Kowarik, A.: Introduction to statistical disclosure control (SDC). Project: Relative to the testing of SDC algorithms and provision of practical SDC, data analysis OG (2013)

    Google Scholar 

  41. Ulltveit-Moe, N., Oleshchuk, V.A.: Measuring privacy leakage for IDS rules. CoRR, abs/1308.5421 (2013)

    Google Scholar 

  42. Ulltveit-Moe, N., Oleshchuk, V.A., Køien, G.M.: Location-aware mobile intrusion detection with enhanced privacy in a 5G context. Wirel. Pers. Commun. 57(3), 317–338 (2011)

    Article  Google Scholar 

  43. Vaidya, J., Clifton, C.W., Zhu, Y.M.: Privacy Preserving Data Mining, vol. 19. Springer, Boston (2006). https://doi.org/10.1007/978-0-387-29489-6

    MATH  Google Scholar 

  44. XACML Obligation Profile for Healthcare Version 1.0, February 2013. http://docs.oasis-open.org/xacml/xspa-obl/v1.0/csd01/xspa-obl-v1.0-csd01.html

  45. Zuech, R., Khoshgoftaar, T.M., Wald, R.: Intrusion detection and big heterogeneous data: a survey. J. Big Data 2(1), 1–41 (2015)

    Article  Google Scholar 

Download references

Acknowledgments

The research leading to these results has received funding from the FP7 EU-funded project SECENTIS (FP7-PEOPLE-2012-ITN, grant no. 317387).

Author information

Authors and Affiliations

  1. DISI, University of Trento, Trento, Italy

    Nadia Metoui

  2. SAP Labs France, Security Research, Sophia-Antipolis, France

    Michele Bezzi

  3. DIBRIS, University of Genova, Genoa, Italy

    Alessandro Armando

Authors
  1. Nadia Metoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michele Bezzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alessandro Armando
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nadia Metoui .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Josef Küng

  3. FAW, University of Linz, Linz, Austria

    Roland Wagner

  4. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  5. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Nam Thoai

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer-Verlag GmbH Germany

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Metoui, N., Bezzi, M., Armando, A. (2017). Risk-Based Privacy-Aware Access Control for Threat Detection Systems. In: Hameurlain, A., Küng, J., Wagner, R., Dang, T., Thoai, N. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVI. Lecture Notes in Computer Science(), vol 10720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56266-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-56266-6_1

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-56265-9

  • Online ISBN: 978-3-662-56266-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation