Abstract
Secure Evaluation of Private Functions (PF-SFE) allows two parties to compute a private function which is known by one party only on private data of both. It is known that PF-SFE can be reduced to Secure Function Evaluation (SFE) of a Universal Circuit (UC). Previous UC constructions only simulated circuits with gates of d = 2 inputs while gates with d > 2 inputs were decomposed into many gates with 2 inputs which is inefficient for large d as the size of UC heavily depends on the number of gates.
We present generalized UC constructions to efficiently simulate any circuit with gates of d ≥ 2 inputs having efficient circuit representation. Our constructions are non-trivial generalizations of previously known UC constructions.
As application we show how to securely evaluate private functions such as neural networks (NN) which are increasingly used in commercial applications. Our provably secure PF-SFE protocol needs only one round in the semi-honest model (or even no online communication at all using non-interactive oblivious transfer) and evaluates a generalized UC that entirely hides the structure of the private NN. This enables applications like privacy-preserving data classification based on private NNs without trusted third party while simultaneously protecting user’s data and NN owner’s intellectual property.
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References
Aiello, W., Ishai, Y., Reingold, O.: Priced oblivious transfer: How to sell digital goods. In: Pfitzmann, B. (ed.) EUROCRYPT 2001. LNCS, vol. 2045, pp. 119–135. Springer, Heidelberg (2001)
Chang, Y.-C., Lu, C.-J.: Oblivious polynomial evaluation and oblivious neural learning. In: Boyd, C. (ed.) ASIACRYPT 2001. LNCS, vol. 2248, pp. 369–384. Springer, Heidelberg (2001)
Drewes, R.: An artifical neural network spam classifier (August 2002), http://www.interstice.com/drewes/cs676/spam-nn/
Gorman, R.P., Sejnowski, T.J.: Analysis of hidden units in a layered network trained to classify sonar targets. Neural Networks 1(1), 75–89 (1988)
Goyal, V., Mohassel, P., Smith, A.: Efficient two party and multi party computation against covert adversaries. In: Smart, N.P. (ed.) EUROCRYPT 2008. LNCS, vol. 4965, pp. 289–306. Springer, Heidelberg (2008)
Gunupudi, V., Tate, S.R.: Generalized non-interactive oblivious transfer using count-limited objects with applications to secure mobile agents. In: Tsudik, G. (ed.) FC 2008. LNCS, vol. 5143, pp. 98–112. Springer, Heidelberg (2008)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2(5), 359–366 (1989)
Ishai, Y., Kilian, J., Nissim, K., Petrank, E.: Extending Oblivious Transfers Efficiently. In: Boneh, D. (ed.) CRYPTO 2003. LNCS, vol. 2729, pp. 145–161. Springer, Heidelberg (2003)
Kolesnikov, V., Schneider, T.: Improved garbled circuit: Free XOR gates and applications. In: Aceto, L., Damgård, I., Goldberg, L.A., Halldórsson, M.M., Ingólfsdóttir, A., Walukiewicz, I. (eds.) ICALP 2008, Part II. LNCS, vol. 5126, pp. 486–498. Springer, Heidelberg (2008)
Kolesnikov, V., Schneider, T.: A practical universal circuit construction and secure evaluation of private functions. In: Tsudik, G. (ed.) FC 2008. LNCS, vol. 5143, pp. 83–97. Springer, Heidelberg (2008), http://thomaschneider.de/FairplayPF
Lindell, Y., Pinkas, B., Smart, N.: Implementing two-party computation efficiently with security against malicious adversaries. In: Ostrovsky, R., De Prisco, R., Visconti, I. (eds.) SCN 2008. LNCS, vol. 5229, pp. 2–20. Springer, Heidelberg (2008)
Lindell, Y., Pinkas, B.: A proof of Yao’s protocol for secure two-party computation. Cryptology ePrint Archive, Report 2004/175 (2004)
Lindell, Y., Pinkas, B.: An efficient protocol for secure two-party computation in the presence of malicious adversaries. In: Naor, M. (ed.) EUROCRYPT 2007. LNCS, vol. 4515, pp. 52–78. Springer, Heidelberg (2007)
Malkhi, D., Nisan, N., Pinkas, B., Sella, Y.: Fairplay — a secure two-party computation system. In: USENIX (2004), http://www.cs.huji.ac.il/project/Fairplay/fairplay.html
Orlandi, C., Piva, A., Barni, M.: Oblivious neural network computing via homomorphic encryption. European Journal of Information Systems (EURASIP) 2007(1), 1–10 (2007)
Pinkas, B.: Cryptographic techniques for privacy-preserving data mining. SIGKDD Explor. Newsl. 4(2), 12–19 (2002)
Piva, A., Caini, M., Bianchi, T., Orlandi, C., Barni, M.: Enhancing privacy in remote data classification. In: New Approaches for Security, Privacy and Trust in Complex Environments (SEC 2008) (2008)
Plagianakos, V.P., Vrahatis, M.N.: Parallel evolutionary training algorithms for hardware-friendly neural networks. Natural Computing 1(2-3), 307–322 (2002)
Rabin, M.O.: How to exchange secrets with oblivious transfer. Technical report, Harward University, Available at Cryptology ePrint Archive, Report 2005/187 (1981)
Sander, T., Young, A., Yung, M.: Non-interactive cryptocomputing for NC 1. In: Proc. 40th IEEE Symp. on Foundations of Comp. Science, FOCS 1999, New York, pp. 554–566. IEEE, Los Alamitos (1999)
Sato, K., Hikawa, H.: Implementation of multilayer neural network with threshold neurons and its analysis. Artificial Life and Robotics 3(3), 170–175 (1999)
Schneider, T.: Practical secure function evaluation. Master’s thesis, University of Erlangen-Nuremberg (2008), http://thomaschneider.de/theses/da/
StatSoft, Inc. STATISTICA Automated Neural Networks (2008), http://www.statsoft.com/products/stat_nn.html
Tebelskis, J.: Speech Recognition using Neural Networks. PhD thesis, School of Computer Science, Pittsburgh (1995)
Valiant, L.G.: Universal circuits (preliminary report). In: STOC 1976, pp. 196–203. ACM Press, New York (1976)
Waksman, A.: A permutation network. J. ACM 15(1), 159–163 (1968)
Yao, A.C.: How to generate and exchange secrets. In: FOCS 1986, Toronto, pp. 162–167. IEEE, Los Alamitos (1986)
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Sadeghi, AR., Schneider, T. (2009). Generalized Universal Circuits for Secure Evaluation of Private Functions with Application to Data Classification. In: Lee, P.J., Cheon, J.H. (eds) Information Security and Cryptology – ICISC 2008. ICISC 2008. Lecture Notes in Computer Science, vol 5461. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00730-9_21
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DOI: https://doi.org/10.1007/978-3-642-00730-9_21
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