Privacy Protection for Multi-Option Problem of Participatory Sensing Using Random Noise Addition

  • Tomomichi Hayakawa National Institute of Technology
  • Teruhisa Hochin Kyoto Institute of Technology
  • Tokuro Matsuo Advanced Institute for Industrial Technology
Keywords: Participatory Sensing, Privacy Protection, Randomized Response, Negative Surveys


The rapid proliferation of smartphone-mounted multiple sensors has been accompanied by the increasing utilization of participatory sensing, which is a type of crowdsourcing by which many users effectively share sensing data. Privacy protection is important for this purpose because the sensing data often contain private information about the users. Existing privacy protection methods do not enable effective and precise data restoration in this application when there is many choices and few data. In this study, we developed a method for addressing this issue. The randomized response method and negative survey method are used to conceal private information contained in individual data by the addition of random noise to the data. Moreover, the proposed method utilizes a novel procedure whereby the transmission is repeated multiple times when selecting one option from multiple options. The proposed method is evaluated by simulation and is found to be more effective than existing methods.


J. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy, and M. B. Srivastava: Participatory Sensing, World Sensor Web Workshop, ACM Sen-Sys 2006, Colorado, Oct 2006.

H. Lu, D. Frauendorfer, M. Rabbi, M. S. Mast, G. T. Chittaranjan, A. T. Campbell, and T. Choudhury: StressSense: Detecting Stress in Unconstrained Acoustic Environments using Smartphones, ACM International Conference on Ubiquitous Computing (UbiComp 2012), pp. 351–360, 2012.

R. LiKamWa, Y. Liu, N. D. Lane, and L. Zhong: MoodScope: Building a Mood Sensor From Smartphone Usage Patterns, The 11th ACM International Conference on Mobile Systems, Applications, and Services (MobiSys 2013), pp. 389–402, 2013.

M. Azizyan, I. Constandache, and R. Roy Choudhury: SurroundSense: Mobile Phone Localization via Ambience Fingerprinting, The 15th Annual International Conference on Mobile Computing and Networking (MobiCom 2009), pp. 261–272, 2009.

N. Maisonneuve, et al.: NoiseTube: Measuring and mapping noise pollution with mobile phones, In: Information Technologies in Environmental Engineering, Springer Berlin Heidelberg, pp. 215–228, 2009.

S. King and P. Brown: Fix My Street or Else: Using the Internet to Voice Local Public Service Concerns, The International Conference on Theory and Practice of Electronic Governance, Macau, pp. 72–80, 2007.

K. L. Huang, S. S. Kanhere, and W. Hu: Preserving Privacy in Participatory Sensing System, Computer Communication, vol. 33, no. 11, pp. 1266–1280, 2010.

G. Drosatos, P. S. Efraimidis, I. N. Athanasiadis, E. D’Hondt, and M. Stevens: A Privacy Preserving Cloud Computing System for Creating Participatory Noise Maps, Proceedings of 36th IEEE International Conference on Computer Software and Applications (COMPSAC2012), pp. 581–586, 2012.

L. Sweeney: k-anonymity: A Model for Protecting Privacy, International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems, vol. 10, no. 5, pp. 557–570, 2002.

P. Samarati: Protecting Respondents Identities in Microdata Release, IEEE Transactions on Knowledge and Data Engineering, vol. 13, no. 6, pp. 1010–1027, 2001.

C. Dwork, F. McSherry, K. Nissim, and A. Smith: Calibrating Noise to Sensitivity in Private Data Analysis, In: Theory of Cryptography, Springer Berlin Heidelberg, pp. 265–284, 2006.

C. Dwork: Differential Privacy, In: Automata, Languages and Programming, Springer Berlin Heidelberg, pp. 1–12, 2006.

S. L. Warner: Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias, Journal of the American Statistical Association, vol. 60, no. 309, pp. 63–69, 1965.

R. Agrawal, R. Srikant, and D. Thomas: Privacy Preserving OLAP, Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 251–262, 2005.

S. Aoki and K. Sezaki: Negative Surveys with Randomized Response Techniques for Privacy-aware Participatory Sensing, IEICE Transactions on Communications, vol. E97-B, no. 4, 2014.

D. Quercia, I. Leontiadis, L. McNamara, C. Mascolo, and J. Crowcroft: SpotME If You Can: Randomized Responses for Location Obfuscation on Mobile Phones, Proceedings of 31st International Conference on Distributed Computing Systems (ICDCS), pp. 363–372, 2011.

F. Esponda: Negative Surveys, ArXiv Mathematics e-prints, 2006.

F. Esponda and V. M. Guerrero: Surveys with Negative Questions for Sensitive Items, Statistics & Probability Letters, vol. 79, no. 24, pp. 2456–2461, 2009.

M. M. Groat, B. Edwards, J. Horey, W. He, and S. Forrest: Enhancing Privacy in Participatory Sensing with Multidimensional Data, Proceedings of IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 144–152, 2012.

S. Aoki and K. Sezaki: Privacy-Preserving Community Sensing for Medical Research with Duplicated Perturbation, Proceedings of the IEEE International Conference on Communications (IEEE ICC), pp. 4252–4257, 2014.

S. Kullback and R. A. Leibler: On Information and Sufficiency, Annals of Mathematical Statistics, vol. 22, p. 79–86, 1951.

J. C. Angulo, J. Antolin, S. Lopez-Rosa, and R. O. Esquivel: Jensen-Shannon Divergence in Conjugate Spaces: The Entropy Excess of Atomic Systems and Sets with Respect to their Constituents, Elsevier Physica A, vol. 389, pp. 899–907, 2010.

B. Fuglede and F. Topsoe: Jensen-Shannon Divergence and Hilbert Space Embedding, IEEE International Symposium on Information Theory, 2004.