Integrating Privacy and Safety Criteria into Planning Tasks

  • Anna Lavygina
  • Alessandra Russo
  • Naranker Dulay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9331)


In this paper we describe a new approach that uses multi-criteria decision making and the analytic hierarchy process (AHP) for integrating privacy and safety criteria into planning tasks. We apply the approach to the journey planning using two criteria: (i) a willingness-to-share-data (WSD) metric to control data disclosure, and (ii) the number of unsatisfied safety preferences (USP) metric to mitigate risky journeys.


Personal safety Information privacy Multi-criteria decision making Analytic hierarchy process Smart city applications 



This work is supported by the 7th Framework EU-FET project ALLOW Ensembles (grant 600792).


  1. 1.
    André, P., et al.: Journey planning based on user needs. In: CHI 2007 Extended Abstracts on Human Factors in Computing Systems, pp. 2025–2030. ACM (2007)Google Scholar
  2. 2.
    Andrikopoulos, V., et al.: A game theoretic approach for managing multi-modal urban mobility systems. In: Proceedings of the 5th International Conference on Applied Human Factors and Ergonomics (AHFE 2014). CRC Press/Taylor & Francis, Kraków, Poland, July 2014Google Scholar
  3. 3.
    Andrikopoulos, V., Bucchiarone, A., Gómez Sáez, S., Karastoyanova, D., Mezzina, C.A.: Towards modeling and execution of collective adaptive systems. In: Lomuscio, A.R., Nepal, S., Patrizi, F., Benatallah, B., Brandić, I. (eds.) ICSOC 2013. LNCS, vol. 8377, pp. 69–81. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  4. 4.
    Barker, T.J., Zabinsky, Z.B.: A multicriteria decision making model for reverse logistics using analytical hierarchy process. Omega 39(5), 558–573 (2011)CrossRefGoogle Scholar
  5. 5.
    Beirão, G., Cabral, J.S.: Understanding attitudes towards public transport and private car: a qualitative study. Transp. Policy 14(6), 478–489 (2007)CrossRefGoogle Scholar
  6. 6.
    Breaux, T.: Privacy requirements in an age of increased sharing. IEEE Softw. 31(5), 24–27 (2014)CrossRefGoogle Scholar
  7. 7.
    Calavia, L.: A semantic autonomous video surveillance system for dense camera networks in smart cities. Sens. 12(8), 10407–10429 (2012)CrossRefGoogle Scholar
  8. 8.
    Caragliu, A., et al.: Smart cities in europe. J. Urban Technol. 18(2), 65–82 (2011)CrossRefGoogle Scholar
  9. 9.
    De Cristofaro, E., Di Pietro, R.: Adversaries and countermeasures in privacy-enhanced urban sensing systems. IEEE Syst. J. 7(2), 311–322 (2013)CrossRefGoogle Scholar
  10. 10.
    Eboli, L., Mazzulla, G.: A methodology for evaluating transit service quality based on subjective and objective measures from the passengers point of view. Transp. Policy 18(1), 172–181 (2011)CrossRefGoogle Scholar
  11. 11.
    Ferraz, F.S., Ferraz, C.A.G.: Smart city security issues: depicting information security issues in the role of an urban environment. In: 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pp. 842–847. IEEE (2014)Google Scholar
  12. 12.
    Flammini, F., Gaglione, A., Mazzocca, N., Pragliola, C.: Quantitative Security risk assessment and management for railway transportation infrastructures. In: Setola, R., Geretshuber, S. (eds.) CRITIS 2008. LNCS, vol. 5508, pp. 180–189. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  13. 13.
    Ho, W., et al.: Multi-criteria decision making approaches for supplier evaluation and selection: a literature review. Eur. J. Oper. Res. 202(1), 16–24 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Kim, J., et al.: Hybrid choice models: principles and recent progress incorporating social influence and nonlinear utility functions. Procedia Environ. Sci. 22, 20–34 (2014)CrossRefGoogle Scholar
  15. 15.
    Koppelman, F.S.: Non-linear utility functions in models of travel choice behavior. Transp. 10(2), 127–146 (1981)CrossRefGoogle Scholar
  16. 16.
    Loukaitou-Sideris, A., Eck, J.E.: Crime prevention and active living. Am. J. Health Promot. 21(4s), 380–389 (2007)CrossRefGoogle Scholar
  17. 17.
    Lynch, G., Atkins, S.: The influence of personal security fears on women’s travel patterns. Transp. 15(3), 257–277 (1988)CrossRefGoogle Scholar
  18. 18.
    Mahmoud, M., Hine, J.: Using AHP to measure the perception gap between current and potential users of bus services. Transp. Plann. Technol. 36(1), 4–23 (2013)CrossRefGoogle Scholar
  19. 19.
    Martinez-Balleste, A., et al.: The pursuit of citizens’ privacy: a privacy-aware smart city is possible. IEEE Commun. Mag. 51(6), 136–141 (2013)CrossRefGoogle Scholar
  20. 20.
    Nam, T., Pardo, T.A.: Conceptualizing smart city with dimensions of technology, people, and institutions. In: Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, pp. 282–291. ACM (2011)Google Scholar
  21. 21.
    Patsakis, C., Solanas, A.: Privacy-aware event data recorders: cryptography meets the automotive industry again. IEEE Commun. Mag. 51(12), 122–128 (2013)CrossRefGoogle Scholar
  22. 22.
    Saaty, T.L.: What is the analytic hierarchy process? In: Mitra, G., Greenberg, H.J., Lootsma, F.A., Rijkaert, M.J., Zimmermann, H.J. (eds.) Mathematical Models for Decision Support. NATO ASI Series, vol. 48, pp. 109–121. Springer, Heidelberg (1988) CrossRefGoogle Scholar
  23. 23.
    Schaffers, H., et al.: Smart cities and the future internet: towards cooperation frameworks for open innovation. In: Domingue, J., et al. (eds.) FI. LNCS, vol. 6656, pp. 431–446. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  24. 24.
    Srinivas, N., Deb, K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Anna Lavygina
    • 1
  • Alessandra Russo
    • 1
  • Naranker Dulay
    • 1
  1. 1.Department of ComputingImperial College LondonLondonUK

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