skip to main content
research-article

Designing Strong Privacy Metrics Suites Using Evolutionary Optimization

Published:21 January 2021Publication History
Skip Abstract Section

Abstract

The ability to measure privacy accurately and consistently is key in the development of new privacy protections. However, recent studies have uncovered weaknesses in existing privacy metrics, as well as weaknesses caused by the use of only a single privacy metric. Metrics suites, or combinations of privacy metrics, are a promising mechanism to alleviate these weaknesses, if we can solve two open problems: which metrics should be combined and how. In this article, we tackle the first problem, i.e., the selection of metrics for strong metrics suites, by formulating it as a knapsack optimization problem with both single and multiple objectives. Because solving this problem exactly is difficult due to the large number of combinations and many qualities/objectives that need to be evaluated for each metrics suite, we apply 16 existing evolutionary and metaheuristic optimization algorithms. We solve the optimization problem for three privacy application domains: genomic privacy, graph privacy, and vehicular communications privacy. We find that the resulting metrics suites have better properties, i.e., higher monotonicity, diversity, evenness, and shared value range, than previously proposed metrics suites.

References

  1. James Alexander and Jonathan Smith. 2003. Engineering privacy in public: Confounding face recognition. In Proceedings of the 3rd International Workshop on Privacy Enhancing Technologies (PET’03) (LNCS 2760). Springer, Dresden, 88--106.Google ScholarGoogle ScholarCross RefCross Ref
  2. Erman Ayday, Jean Louis Raisaro, Jean-Pierre Hubaux, and Jacques Rougemont. 2013. Protecting and evaluating genomic privacy in medical tests and personalized medicine. In Proceedings of the 12th ACM Workshop on Workshop on Privacy in the Electronic Society (WPES’13). ACM, New York, NY, 95--106. DOI:https://doi.org/10.1145/2517840.2517843Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Thomas Bäck. 2000. Evolutionary Computation 2: Advanced Algorithms and Operators. Taylor 8 Francis Ltd.Google ScholarGoogle Scholar
  4. Thomas Bäck, D. B. Fogel, and Z. Michalewicz. 2000. Evolutionary Computation 1: Basic Algorithms and Operators (1st ed.). CRC Press.Google ScholarGoogle Scholar
  5. Vitor Basto-Fernandes, Iryna Yevseyeva, André Deutz, and Michael Emmerich. 2017. A survey of diversity oriented optimization: Problems, indicators, and algorithms. In EVOLVE—A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation VII. Springer International Publishing, Cham, 3--23. DOI:https://doi.org/10.1007/978-3-319-49325-1_1Google ScholarGoogle Scholar
  6. Elisa Bertino, Dan Lin, and Wei Jiang. 2008. A survey of quantification of privacy preserving data mining algorithms. In Privacy-Preserving Data Mining: Models and Algorithms. Number 34 in Advances in Database Systems. Springer, Chapter 8, 183--205.Google ScholarGoogle Scholar
  7. Hans-Georg Beyer and Bernhard Sendhoff. 2008. Covariance matrix adaptation revisited—The CMSA evolution strategy. In Parallel Problem Solving from Nature—PPSN X (Lecture Notes in Computer Science). Springer, Berlin, 123--132.Google ScholarGoogle Scholar
  8. Francesco Biscani, Dario Izzo, Wenzel Jakob, Marcus Märtens, Alessio Mereta, Cord Kaldemeyer, Sergey Lyskov, Sylvain Corlay, Benjamin Pritchard, Kishan Manani, Johan Mabille, Tomasz Miąsko, Axel Huebl, jakirkham, hulucc, polygon, Zihao Fu, The Gitter Badger, Merlin Nimier-David, Luka Čehovin Zajc, Jonas Adler, John Travers, Jeongseok Lee, Jakob Jordan, Ivan Smirnov, Huu Nguyen, Felipe Lema, Erik O’Leary, and Andrea Mambrini. 2019. Esa/Pagmo2: Pagmo 2.10. Retrieved from https://zenodo.org/record/2529931/export/hx. DOI:https://doi.org/10.5281/zenodo.2529931Google ScholarGoogle Scholar
  9. Julian Blank and Kalyanmoy Deb. 2019. Pymoo—Multi-Objective Optimization in Python. Retrieved from https://pymoo.org.Google ScholarGoogle Scholar
  10. Kalyanmoy Deb and Himanshu Jain. 2014. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints. IEEE Trans. Evolution. Comput. 18, 4 (2014), 577--601. DOI:https://doi.org/10.1109/TEVC.2013.2281535Google ScholarGoogle ScholarCross RefCross Ref
  11. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 2 (Apr. 2002), 182--197. DOI:https://doi.org/10.1109/4235.996017Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Claudia Diaz, Stefaan Seys, Joris Claessens, and Bart Preneel. 2003. Towards measuring anonymity. In Privacy Enhancing Technologies, Roger Dingledine and Paul Syverson (Eds.). Number 2482 in Lecture Notes in Computer Science. Springer, Berlin, 54--68.Google ScholarGoogle Scholar
  13. Claudia Diaz, Carmela Troncoso, and George Danezis. 2007. Does additional information always reduce anonymity? In Proceedings of the ACM Workshop on Privacy in Electronic Society (WPES’07). ACM, Alexandria, VA, 72--75. DOI:https://doi.org/10.1145/1314333.1314347Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. David Eckhoff and Christoph Sommer. 2018. Readjusting the privacy goals in vehicular ad-hoc networks: A safety-preserving solution using non-overlapping time-slotted pseudonym pools. Comput. Commun. 122 (June 2018), 118--128. DOI:https://doi.org/10.1016/j.comcom.2018.03.006Google ScholarGoogle Scholar
  15. Michael Emmerich, Nicola Beume, and Boris Naujoks. 2005. An emo algorithm using the hypervolume measure as selection criterion. In Evolutionary Multi-Criterion Optimization. Springer, Berlin, 62--76.Google ScholarGoogle Scholar
  16. Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, and Christian Gagné. 2012. DEAP: Evolutionary algorithms made easy. J. Mach. Learn. Res. 13 (2012), 2171--2175. Issue Jul.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. David E. Goldberg. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading, MA.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. David Hadka. 2019. A Free and Open Source Python Library for Multiobjective Optimization: Project-Platypus/Platypus. Retrieved from https://github.com/Project-Platypus/Platypus.Google ScholarGoogle Scholar
  19. M. P. Hansen and A. Jaszkiewicz. 1998. Evaluating the Quality of Approximations to the Non-Dominated Set. Technical Report IMM Technical Report IMMREP1998-7. Technical University of Denmark.Google ScholarGoogle Scholar
  20. N. Hansen and A. Ostermeier. 1996. Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation. In Proceedings of IEEE International Conference on Evolutionary Computation. 312--317. DOI:https://doi.org/10.1109/ICEC.1996.542381Google ScholarGoogle ScholarCross RefCross Ref
  21. Nikolaus Hansen and Andreas Ostermeier. 2001. Completely derandomized self-adaptation in evolution strategies. Evolution. Comput. 9, 2 (2001), 159--195. DOI:https://doi.org/10.1162/106365601750190398Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Daojing He, S. Chan, and M. Guizani. 2015. Privacy and incentive mechanisms in people-centric sensing networks. IEEE Commun. Mag. 53, 10 (Oct. 2015), 200--206. DOI:https://doi.org/10.1109/MCOM.2015.7295484Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Christian Igel, Nikolaus Hansen, and Stefan Roth. 2007. Covariance matrix adaptation for multi-objective optimization. Evolution. Comput. 15, 1 (2007), 1--28. DOI:https://doi.org/10.1162/evco.2007.15.1.1Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Himanshu Jain and Kalyanmoy Deb. 2014. An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part II: Handling constraints and extending to an adaptive approach. IEEE Trans. Evolution. Comput. 18, 4 (Aug. 2014), 602--622. DOI:https://doi.org/10.1109/TEVC.2013.2281534Google ScholarGoogle ScholarCross RefCross Ref
  25. Georgios Kalogridis, Costas Efthymiou, Stojan Z. Denic, Tim A. Lewis, and Rafael Cepeda. 2010. Privacy for smart meters: Towards undetectable appliance load signatures. In Proceedings of the 1st International Conference on Smart Grid Communications (SmartGridComm’10). IEEE, Gaithersburg, MD, 232--237.Google ScholarGoogle ScholarCross RefCross Ref
  26. Hans Kellerer, Ulrich Pferschy, and David Pisinger. 2004. Multidimensional knapsack problems. In Knapsack Problems. Springer, Berlin, 235--283. DOI:https://doi.org/10.1007/978-3-540-24777-7_9Google ScholarGoogle Scholar
  27. J. Kennedy and R. Eberhart. 1995. Particle swarm optimization. In Proceedings of the International Conference on Neural Networks (ICNN’95), Vol. 4. 1942--1948. DOI:https://doi.org/10.1109/ICNN.1995.488968Google ScholarGoogle Scholar
  28. S. Kukkonen and J. Lampinen. 2005. GDE3: The third evolution step of generalized differential evolution. In Proceedings of the IEEE Congress on Evolutionary Computation, Vol. 1. IEEE, 443--450. DOI:https://doi.org/10.1109/CEC.2005.1554717Google ScholarGoogle Scholar
  29. Marco Laumanns, Lothar Thiele, Kalyanmoy Deb, and Eckart Zitzler. 2002. Combining convergence and diversity in evolutionary multiobjective optimization. Evolution. Comput. 10, 3 (Sept. 2002), 263--282. DOI:https://doi.org/10.1162/106365602760234108Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Marco Laumanns, Lothar Thiele, and Eckart Zitzler. 2001. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. TIK-Report 103. Eidgenössische Technische Hochschule Zürich (ETH).Google ScholarGoogle Scholar
  31. Averill M. Law and W. David Kelton. 2000. Simulation Modelling and Analysis, 3rd ed. McGraw-Hill Education, Boston, MA.Google ScholarGoogle Scholar
  32. Longmei Li, Iryna Yevseyeva, Vitor Basto-Fernandes, Heike Trautmann, Ning Jing, and Michael Emmerich. 2017. Building and using an ontology of preference-based multiobjective evolutionary algorithms. In Evolutionary Multi-Criterion Optimization. Vol. 10173. Springer International Publishing, Cham, 406--421. DOI:https://doi.org/10.1007/978-3-319-54157-0_28Google ScholarGoogle Scholar
  33. Miqing Li and Xin Yao. 2019. Quality evaluation of solution sets in multiobjective optimisation: A survey. ACM Comput. Surv. 52, 2 (Mar. 2019), 26:1--26:38. DOI:https://doi.org/10.1145/3300148Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Silvano Martello, David Pisinger, and Paolo Toth. 2000. New trends in exact algorithms for the 0--1 knapsack problem. Eur. J. Oper. Res. 123, 2 (2000), 325--332. DOI:https://doi.org/10.1016/S0377-2217(99)00260-XGoogle ScholarGoogle ScholarCross RefCross Ref
  35. Silvano Martello and Paolo Toth. 1990. Knapsack Problems: Algorithms and Computer Implementations. John Wiley 8 Sons.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Kaisa Miettinen. 1998. Nonlinear Multiobjective Optimization. Springer Science 8 Business Media.Google ScholarGoogle Scholar
  37. Rida E. Moustafa. 2011. Parallel coordinate and parallel coordinate density plots. Wiley Interdisc. Rev.: Comput. Stat. 3, 2 (2011), 134--148. DOI:https://doi.org/10.1002/wics.145Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Arvind Narayanan and Vitaly Shmatikov. 2009. De-anonymizing social networks. In Proceedings of the IEEE Symposium on Security and Privacy. IEEE, Oakland, CA, 173--187. DOI:https://doi.org/10.1109/SP.2009.22Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. A. J. Nebro, J. J. Durillo, J. Garcia-Nieto, C. A. Coello Coello, F. Luna, and E. Alba. 2009. SMPSO: A new PSO-based metaheuristic for multi-objective optimization. In Proceedings of the IEEE Symposium on Computational Intelligence in Milti-Criteria Decision-Making. IEEE, 66--73. DOI:https://doi.org/10.1109/MCDM.2009.4938830Google ScholarGoogle Scholar
  40. J. A. Nelder and R. Mead. 1965. A simplex method for function minimization. Comput. J. 7, 4 (1965), 308--313. DOI:https://doi.org/10.1093/comjnl/7.4.308Google ScholarGoogle ScholarCross RefCross Ref
  41. Simon Oya, Carmela Troncoso, and Fernando Pérez-González. 2017. Back to the drawing board: Revisiting the design of optimal location privacy-preserving mechanisms. In Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS’17). ACM, Dallas, TX, 1959--1972. DOI:https://doi.org/10.1145/3133956.3134004Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Thomas Harvey Rowan. 1990. Functional Stability Analysis of Numerical Algorithms. Ph.D. Dissertation. University of Texas at Austin.Google ScholarGoogle Scholar
  43. Jason R. Schott. 1995. Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization.Technical Report AFIT/CI/CIA-95-039. Air Force Inst of Tech Wright-Patterson AFB OH.Google ScholarGoogle Scholar
  44. Richard M. Shiffrin and Robert M. Nosofsky. 1994. Seven plus or minus two: A commentary on capacity limitations. Psychol. Rev. 101, 2 (1994), 357--361. DOI:https://doi.org/10.1037/0033-295X.101.2.357Google ScholarGoogle ScholarCross RefCross Ref
  45. Reza Shokri, George Theodorakopoulos, Jean-Yves Le Boudec, and Jean-Pierre Hubaux. 2011. Quantifying location privacy. In Proceedings of the IEEE Symposium on Security and Privacy (S8P’11). IEEE, 247--262. DOI:https://doi.org/10.1109/SP.2011.18Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Margarita Reyes Sierra and Carlos A. Coello Coello. 2005. Improving PSO-based multi-objective optimization using crowding, mutation and -dominance. In Evolutionary Multi-Criterion Optimization (Lecture Notes in Computer Science). Springer, Berlin, 505--519.Google ScholarGoogle Scholar
  47. Rainer Storn and Kenneth Price. 1997. Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 4 (1997), 341--359. DOI:https://doi.org/10.1023/A:1008202821328Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Paul Syverson. 2013. Why I’m not an entropist. In Proceedings of the 17th International Workshop on Security Protocols (LNCS 7028). Springer, Cambridge, UK, 213--230.Google ScholarGoogle Scholar
  49. El-Ghazali Talbi. 2009. Metaheuristics: From Design to Implementation. John Wiley 8 Sons.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Chris Tofallis. 2014. Add or multiply? A tutorial on ranking and choosing with multiple criteria. INFORMS Trans. Edu. 14, 3 (2014), 109--119. DOI:https://doi.org/10.1287/ited.2013.0124Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Evangelos Triantaphyllou. 2000. Multi-Criteria Decision Making Methods: A Comparative Study. Springer U.S.Google ScholarGoogle ScholarCross RefCross Ref
  52. David A. Van Veldhuizen and Gary B. Lamont. 1998. Evolutionary computation and convergence to a Pareto front. In Late Breaking Papers at the Genetic Programming 1998 Conference. Omni Press, 221--228.Google ScholarGoogle Scholar
  53. Isabel Wagner. 2017. Evaluating the strength of genomic privacy metrics. ACM Trans. Priv. Secur. 20, 1 (Jan. 2017), 2:1--2:34. DOI:https://doi.org/10.1145/3020003Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Isabel Wagner and David Eckhoff. 2018. Technical privacy metrics: A systematic survey. ACM Comput. Surv. 51, 3 (2018), 57:1--57:38. DOI:https://doi.org/10.1145/3168389Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Isabel Wagner and Iryna Yevseyeva. 2020. Privacy Metrics Suites for Genomic Privacy, Vehicular Communications Privacy, and Graph Privacy (Version 1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3350563Google ScholarGoogle Scholar
  56. Simon Wessing. 2017. Evoalgos: Modular Evolutionary Algorithms. Retrieved from https://ls11-www.cs.tu-dortmund.de/people/swessing/evoalgos/doc/.Google ScholarGoogle Scholar
  57. Iryna Yevseyeva, Andreia P. Guerreiro, Michael T. M. Emmerich, and Carlos M. Fonseca. 2014. A portfolio optimization approach to selection in multiobjective evolutionary algorithms. In Proceedings of the Conference on Parallel Problem Solving from Nature (PPSN’14). Vol. 8672. Springer International Publishing, Cham, 672--681. DOI:https://doi.org/10.1007/978-3-319-10762-2_66Google ScholarGoogle Scholar
  58. Q. Zhang and H. Li. 2007. MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evolution. Comput. 11, 6 (Dec. 2007), 712--731. DOI:https://doi.org/10.1109/TEVC.2007.892759Google ScholarGoogle Scholar
  59. Y. Zhao and I. Wagner. 2019. On the strength of privacy metrics for vehicular communication. IEEE Trans. Mobile Comput. 18, 2 (Feb. 2019), 390--403. DOI:https://doi.org/10.1109/TMC.2018.2830359Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. Yuchen Zhao and Isabel Wagner. 2020. Using metrics suites to improve the measurement of privacy in graphs. IEEE Trans. Depend. Secure Comput. (2020). DOI:https://doi.org/10.1109/TDSC.2020.2980271Google ScholarGoogle ScholarCross RefCross Ref
  61. Eckart Zitzler and Simon Künzli. 2004. Indicator-based selection in multiobjective search. In Proceedings of the Conference on Parallel Problem Solving from Nature (PPSN’04). Vol. 3242. Springer, Berlin, 832--842.Google ScholarGoogle ScholarCross RefCross Ref
  62. Eckart Zitzler and Lothar Thiele. 1998. Multiobjective optimization using evolutionary algorithms—A comparative case study. In Proceedings of the Conference on Parallel Problem Solving from Nature (PPSN’98). Vol. 1498. Springer, Berlin, 292--301. DOI:https://doi.org/10.1007/BFb0056872Google ScholarGoogle ScholarCross RefCross Ref
  63. E. Zitzler and L. Thiele. Nov./1999. Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Trans. Evolution. Comput. 3, 4 (Nov. 1999), 257--271. DOI:https://doi.org/10.1109/4235.797969Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Designing Strong Privacy Metrics Suites Using Evolutionary Optimization

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in

            Full Access

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader

            HTML Format

            View this article in HTML Format .

            View HTML Format
            About Cookies On This Site

            We use cookies to ensure that we give you the best experience on our website.

            Learn more

            Got it!