Privacy-Preserving Emotion Detection for Crowd Management

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• 作者：Zeki Erkin (19)
  Jie Li (20)
  Arnold P. O. S. Vermeeren (20)
  Huib de Ridder (20)
  
• 关键词：Emotion detection ; crowd management ; privacy ; homomorphic encryption
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8610
• 期：1
• 页码：359-370
• 全文大小：277 KB
• 参考文献：1. Levenson, R.W.: The intrapersonal functions of emotion. Cognition & Emotion聽13(5), 481鈥?04 (1999) <a class="external" href="http://dx.doi.org/10.1080/026999399379159" target="_blank" title="It opens in new window">CrossRefa>
  2. Arnold, M.B.: Emotion and Personality: Psychological Aspects, vol.聽1. Colombia University Press, New York (1960)
  3. Li, J., Erkin, Z., de Ridder, H., Vermeeren, A.: A field study on real-time self-reported emotions in crowds. In: Proceedings of ICT OPEN 2013, Eindhoven, The Netherlands (2013)
  4. Bianchi, T., Piva, A., Barni, M.: Composite signal representation for fast and storage-efficient processing of encrypted signals. IEEE Transactions on Signal Processing (2009)
  5. Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: SIGMOD 2000: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, vol.聽29(2), pp. 439鈥?50. ACM Press, New York (2000)
  6. Lindell, Y., Pinkas, B.: Privacy preserving data mining. Journal of Cryptology, pp. 36鈥?4 (2000)
  7. Lagendijk, R.L., Erkin, Z., Barni, M.: Encrypted signal processing for privacy protection: Conveying the utility of homomorphic encryption and multiparty computation. IEEE Signal Process. Mag.聽30(1), 82鈥?05 (2013) <a class="external" href="http://dx.doi.org/10.1109/MSP.2012.2219653" target="_blank" title="It opens in new window">CrossRefa>
  8. Melchor, C.A., Fau, S., Fontaine, C., Gogniat, G., Sirdey, R.: Recent advances in homomorphic encryption: A possible future for signal processing in the encrypted domain. IEEE Signal Process. Mag.聽30(1), 108鈥?17 (2013) <a class="external" href="http://dx.doi.org/10.1109/MSP.2012.2230219" target="_blank" title="It opens in new window">CrossRefa>
  9. Goldreich, O.: Foundations of Cryptography II. Cambridge University Press (2004)
  10. Hirt, M.: Receipt-free / k-out-of- / l voting based on elgamal encryption. In: Chaum, D., Jakobsson, M., Rivest, R.L., Ryan, P.Y.A., Benaloh, J., Kutylowski, M., Adida, B. (eds.) Towards Trustworthy Elections. LNCS, vol.聽6000, pp. 64鈥?2. Springer, Heidelberg (2010) <a class="external" href="http://dx.doi.org/10.1007/978-3-642-12980-3_3" target="_blank" title="It opens in new window">CrossRefa>
  11. Barni, M., Failla, P., Lazzeretti, R., Sadeghi, A.R., Schneider, T.: Privacy-preserving ecg classification with branching programs and neural networks. IEEE Transactions on Information Forensics and Security聽6(2), 452鈥?68 (2011) <a class="external" href="http://dx.doi.org/10.1109/TIFS.2011.2108650" target="_blank" title="It opens in new window">CrossRefa>
  12. Erkin, Z., Franz, M., Guajardo, J., Katzenbeisser, S., Lagendijk, I., Toft, T.: Privacy-preserving face recognition. In: Goldberg, I., Atallah, M.J. (eds.) PETS 2009. LNCS, vol.聽5672, pp. 235鈥?53. Springer, Heidelberg (2009) <a class="external" href="http://dx.doi.org/10.1007/978-3-642-03168-7_14" target="_blank" title="It opens in new window">CrossRefa>
  13. Erkin, Z., Veugen, T., Toft, T., Lagendijk, R.L.: Generating private recommendations efficiently using homomorphic encryption and data packing. IEEE Transactions on Information Forensics and Security聽7(3), 1053鈥?066 (2012) <a class="external" href="http://dx.doi.org/10.1109/TIFS.2012.2190726" target="_blank" title="It opens in new window">CrossRefa>
  14. Kononchuk, D., Erkin, Z., van der Lubbe, J.C.A., Lagendijk, R.L.: Privacy-preserving user data oriented services for groups with dynamic participation. In: Crampton, J., Jajodia, S., Mayes, K. (eds.) ESORICS 2013. LNCS, vol.聽8134, pp. 418鈥?42. Springer, Heidelberg (2013) <a class="external" href="http://dx.doi.org/10.1007/978-3-642-40203-6_24" target="_blank" title="It opens in new window">CrossRefa>
  15. Jagannathan, G., Wright, R.N.: Privacy-preserving distributed k-means clustering over arbitrarily partitioned data. In: KDD, pp. 593鈥?99 (2005)
  16. Beye, M., Erkin, Z., Lagendijk, R.L.: Efficient privacy preserving k-means clustering in a three-party setting. In: IEEE Workshop on Information Forensics and Security, pp. 1鈥? (2011)
  17. Paillier, P.: Public-Key Cryptosystems Based on Composite Degree Residuosity Classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol.聽1592, pp. 223鈥?38. Springer, Heidelberg (1999) <a class="external" href="http://dx.doi.org/10.1007/3-540-48910-X_16" target="_blank" title="It opens in new window">CrossRefa>
  18. Damg氓rd, I.B., Nielsen, J.B.: Universally composable efficient multiparty computation from threshold homomorphic encryption. In: Boneh, D. (ed.) CRYPTO 2003. LNCS, vol.聽2729, pp. 247鈥?64. Springer, Heidelberg (2003) <a class="external" href="http://dx.doi.org/10.1007/978-3-540-45146-4_15" target="_blank" title="It opens in new window">CrossRefa>
  19. Chaum, D.: Untraceable electronic mail, return addresses, and digital pseudonyms. Commun. ACM聽24(2), 84鈥?8 (1981) <a class="external" href="http://dx.doi.org/10.1145/358549.358563" target="_blank" title="It opens in new window">CrossRefa>
  
• 作者单位：Zeki Erkin (19)
  Jie Li (20)
  Arnold P. O. S. Vermeeren (20)
  Huib de Ridder (20)
  
  19. Cyber Security Group, Department of Intelligent Systems, Delft University of Technology, 2628 CD, Delft, The Netherlands
  20. Persuasive Experience Research, Industrial Design Engineering, Delft University of Technology, 2628 CD, Delft, The Netherlands
  
• ISSN：1611-3349

文摘

Emotion detection plays a vital role in crowd management as it enables social event organizers to detect the actions of masses and react accordingly. There are several approaches to detect emotions in a crowd, including surveillance cameras, human observers and sensors. One other approach to gather emotion data is self-reporting. A recent study showed that self-reporting is feasible, reliable and efficient. However, there is a strong privacy concern among people that risks the use of such self-reporting mechanisms in wide use. In this work, we address the privacy aspect of self-reporting mechanism and propose a cryptographic approach that hides the sensitive data from the organizers but permits to compute statistical data for crowd management. The feasibility of using cryptography in real life for privacy protection is also investigated in terms of complexity.