Abstract
In this paper we consider a scenario where a user wants to outsource her documents to the cloud, so that she can later reliably delegate (to the cloud) pattern matching operations on these documents. We propose an efficient solution to this problem that relies on the homomorphic MAC for polynomials proposed by Catalano and Fiore in [14]. Our main contribution are new methods to express pattern matching operations (both in their exact and approximate variants) as low degree polynomials, i.e. polynomials whose degree solely depends on the size of the pattern. To better assess the practicality of our schemes, we propose a concrete implementation that further optimizes the efficiency of the homomorphic MAC from [14]. Our implementation shows that the proposed protocols are extremely efficient for the client, while remaining feasible at server side.
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Notes
- 1.
In particular the degree of these polynomials solely depends on the size of pattern string and is independent of the size of the texts.
- 2.
Notice that in [14] a solution where the size of \(\pi \) can be made independent of d is also proposed. This solution however is computationally much less efficient as it imposes larger parameters.
- 3.
Indeed, our first implementations show that this cost can quickly become unbearable even for texts of few thousands characters.
- 4.
Moreover this pre-processing has to be done by the text owner (the weak client in our scenario) and cannot be delegated to the untrusted cloud server.
- 5.
Recall that in the homomorphic MAC scheme from [14] the size of the tags grows with the degree of the arithmetic circuit.
- 6.
Notice that the fact that we consider low degree polynomials is crucial here. Our technique is efficient solely because \(\ell \) does not need to be too big to be able to interpolate correctly at the end.
- 7.
These timings are not reported in this paper but are available upon request.
- 8.
We stress that we focused on our optimized techniques, as they are better than the alternative solutions discussed before in essentially all settings considered here.
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Acknowledgements
This research was supported in part by a FIR 2014 grant by the University of Catania. Thanks to Nuno Tiago Ferreira de Carvalho for his Homomorphic MACs library (Available at https://bitbucket.org/ntfc/cf-homomorphic-mac/).
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Catalano, D., Di Raimondo, M., Faro, S. (2016). Verifiable Pattern Matching on Outsourced Texts. In: Zikas, V., De Prisco, R. (eds) Security and Cryptography for Networks. SCN 2016. Lecture Notes in Computer Science(), vol 9841. Springer, Cham. https://doi.org/10.1007/978-3-319-44618-9_18
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