Abstract
Neural networks are a fundamental tool in data classification. A protocol whereby a user may ask a service provider to run a neural network on an input provided in encrypted format is proposed here, in such a way that the neural network owner does not get any knowledge about the processed data. At the same time, the knowledgeb embeddedwithin the network itself is protected.With respect to previousworks in this field, the interaction between the user and the neural network owner is kept to a minimum without resorting to general secure multi-party computation protocols.
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Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: SIGMOD ’00: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 439–450 (2000)
Barker, E., Barker, W., Burr, W., Polk, W., Smid, M.: Recommendation for Key Management–Part 1: General. NIST Special Publication pp. 800–57 (2005)
Barni, M., Orlandi, C., Piva, A.: A privacy-preserving protocol for neural-network-based computation. In: MM&Sec ’06: Proceeding of the 8th Workshop on Multimedia and Security, pp. 146–151 (2006)
Beaver, D.: Minimal-latency secure function evaluation. Proc. of EUROCRYPT00, LNCS pp. 335–350 (2000)
Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press (1995)
Chang, Y., Lu, C.: Oblivious polynomial evaluation and oblivious neural learning. Theoretical Computer Science 341(1), 39–54 (2005)
Comesana, P., Perez-Freire, L., Perez-Gonzalez, F.: Blind newton sensitivity attack. Information Security, IEE Proceedings 153(3), 115–125 (2006)
Damgrård, I., Jurik, M.: A generalisation, a simplification and some applications of Paillier’s probabilistic public-key system. In: Public Key Cryptography, pp. 119–136 (2001)
Dingledine, R., Mathewson, N., Syverson, P.: Tor: The second-generation onion router. In: Proceedings of the 13th USENIX Security Symposium (2004)
Fouque, P., Stern, J., Wackers, G.: CryptoComputing with rationals. Financial-Cryptography.-6th-International-Conference, Lecture-Notesin-Computer-Science 2357, 136–46 (2003)
Goldreich, O., Micali, S.,Wigderson, A.: How to play any mental game or a completeness theorem for protocols with honest majority. In: STOC ’87: Proceedings of the twentieth annual ACM symposium on Theory of computing, pp. 218–229 (1987)
Gorman, P., et al.: UCI machine learning repository (1988). URL http://archive.ics.uci.edu/ml/
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)
Jha, S., Kruger, L., McDaniel, P.: Privacy Preserving Clustering. 10th ESORICS (2005)
Kalker, T., Linnartz, J.P.M.G., van Dijk,M.:Watermark estimation through detector analysis. In: ICIP98, vol. I, pp. 425–429. Chicago, IL, USA (1998)
Kantarcioglu, M., Vaidya, J.: Privacy preserving naive bayes classifier for horizontally partitioned data. In: In IEEE Workshop on Privacy Preserving Data Mining (2003)
Lapedes, A., Farber, R.: How neural nets work. Neural information processing systems pp. 442–456 (1988)
Laur, S., Lipmaa, H., Mielikäinen, T.: Cryptographically private support vector machines. In: KDD ’06: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge discovery and data mining, pp. 618–624 (2006)
Lindell, Y., Pinkas, B.: Privacy preserving data mining. In Advances in Cryptology - CRYPTO ’00, volume 1880 of Lecture Notes in Computer Science 1880, 36–54 (2000)
Pailler, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Proceedings of Eurocrypt’99, Lecture Notes is Computer Science vol. 1592, pp. 223–238. Springer-Verlag (1999)
Rajkovic, V., et al.: UCI machine learning repository (1997). URL http://archive.ics.uci.edu/ml/
Rumelhart, D., Hinton, G., Williams, R.: Learning internal representations by error propogation. Parallel distributed processing: Explorations in the microstructure of cognition 1, 318–362 (1986)
Sander, T., Young, A., Yung, M.: Non-Interactive CryptoComputing For NC 1. IEEE Symposium on Foundations of Computer Science pp. 554–567 (1999)
Yang, Z., Wright, R.N.: Improved privacy-preserving bayesian network parameter learning on vertically partitioned data. In: ICDEW ’05: Proceedings of the 21st International Conference on Data Engineering Workshops, p. 1196 (2005)
Yao, A.: How to generate and exchange secrets. In: IEEE FOCS’86 - Foundations of Computer Science, pp. 162–167 (1986)
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Piva, A., Orlandi, C., Caini, M., Bianchi, T., Barni, M. (2008). Enhancing Privacy in Remote Data Classification. In: Jajodia, S., Samarati, P., Cimato, S. (eds) Proceedings of The Ifip Tc 11 23rd International Information Security Conference. SEC 2008. IFIP – The International Federation for Information Processing, vol 278. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09699-5_3
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