Abstract
Decentralized Multi-Client Functional Encryption (DMCFE) is a multi-user extension of Functional Encryption (FE) without relying on a trusted third party. However, a fundamental requirement for DMCFE is that the decryptor must collect the partial functional keys and the ciphertexts from all clients. If one client does not generate the partial functional key or the ciphertext, the decryptor cannot obtain any useful information. We found that this strong requirement limits the application of DMCFE in scenarios such as statistical analysis and machine learning.
In this paper, we first introduce a new primitive named Robust Decentralized Multi-Client Functional Encryption (RDMCFE), a notion generalized from DMCFE that aims to tolerate the problem of negative clients leading to nothing for the decryptor, where negative clients represent participants that are unable or unwilling to compute the partial functional key or the ciphertext. Conversely, a client is said to be a positive one if it is able and willing to compute both the partial functional key and the ciphertext. In RDMCFE scheme, the positive client set S is known by each positive client such that the generated partial functional keys help to eliminate the influence of negative clients, and the decryptor can learn the function value corresponding to the sensitive data of all positive clients when the cardinality of the set S is not less than a given threshold. We present such constructions for functionalities corresponding to the evaluation of inner products.
-
1.
We provide a basic RDMCFE construction through the technique of double-masking structure, which is inspired by the work of Bonawitz et al. (CCS 2017). The storage and communication overheads of the construction are small and independent of the length of the vector. However, in the basic construction, for the security guarantee, one set of secret keys can be used to generate partial functional keys for only one function.
-
2.
We show how to design the enhanced construction so that partial functional keys for different functions can be generated with the same set of secret keys, at the cost of increasing storage and communication overheads. Specifically, in the enhanced RDMCFE construction, we protect the mask through a single-input FE scheme and a threshold secret sharing scheme having the additively homomorphic property.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
This form implies that under the same threshold t and the set [n], the algorithm \({\textsf{SS}}.\textsf{Share}(\cdot ,t,[n])\) is run m times, where each time inputting a component of the vector \(\boldsymbol{r}_i\), and m shares with the same subscript are combined into a vector \(\boldsymbol{r}_{i,j}\).
References
Abdalla, M., Bourse, F., De Caro, A., Pointcheval, D.: Simple functional encryption schemes for inner products. In: Katz, J. (ed.) PKC 2015. LNCS, vol. 9020, pp. 733–751. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46447-2_33
Abdalla, M., Benhamouda, F., Gay, R.: From single-input to multi-client inner-product functional encryption. In: Galbraith, S.D., Moriai, S. (eds.) ASIACRYPT 2019. LNCS, vol. 11923, pp. 552–582. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-34618-8_19
Abdalla, M., Benhamouda, F., Kohlweiss, M., Waldner, H.: Decentralizing inner-product functional encryption. In: Lin, D., Sako, K. (eds.) PKC 2019. LNCS, vol. 11443, pp. 128–157. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17259-6_5
Abdalla, M., Catalano, D., Fiore, D., Gay, R., Ursu, B.: Multi-input functional encryption for inner products: function-hiding realizations and constructions without pairings. In: Shacham, H., Boldyreva, A. (eds.) CRYPTO 2018. LNCS, vol. 10991, pp. 597–627. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96884-1_20
Agrawal, S., Clear, M., Frieder, O., Garg, S., O’Neill, A., Thaler, J.: Ad hoc multi-input functional encryption. In: Vidick, T. (ed.) ITCS 2020, vol. 151, pp. 40:1–40:41. Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)
Abdalla, M., Catalano, D., Gay, R., Ursu, B.: Inner-product functional encryption with fine-grained access control. In: Moriai, S., Wang, H. (eds.) ASIACRYPT 2020. LNCS, vol. 12493, pp. 467–497. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64840-4_16
Angel, S., Chen, H., Laine, K., Setty, S.T.V.: PIR with compressed queries and amortized query processing. In: 2018 IEEE Symposium on Security and Privacy, pp. 962–979. IEEE Computer Society Press (2018)
Abdalla, M., Gay, R., Raykova, M., Wee, H.: Multi-input inner-product functional encryption from pairings. In: Coron, J.-S., Nielsen, J.B. (eds.) EUROCRYPT 2017. LNCS, vol. 10210, pp. 601–626. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56620-7_21
Agrawal, S., Goyal, R., Tomida, J.: Multi-input quadratic functional encryption from pairings. In: Malkin, T., Peikert, C. (eds.) CRYPTO 2021. LNCS, vol. 12828, pp. 208–238. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-84259-8_8
Agrawal, S., Goyal, R., Tomida, J.: Multi-input quadratic functional encryption: Stronger security, broader functionality. In: Kiltz, E., Vaikuntanathan, V. (eds.) TCC 2022. LNCS, vol. 13747, pp. 711–740. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-22318-1_25
Ananth, P., Jain, A.: Indistinguishability obfuscation from compact functional encryption. In: Gennaro, R., Robshaw, M. (eds.) CRYPTO 2015. LNCS, vol. 9215, pp. 308–326. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-47989-6_15
Ananth, P., Lombardi, A.: Succinct garbling schemes from functional encryption through a local simulation paradigm. In: Beimel, A., Dziembowski, S. (eds.) TCC 2018. LNCS, vol. 11240, pp. 455–472. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03810-6_17
Agrawal, S., Libert, B., Maitra, M., Titiu, R.: Adaptive simulation security for inner product functional encryption. In: Kiayias, A., Kohlweiss, M., Wallden, P., Zikas, V. (eds.) PKC 2020. LNCS, vol. 12110, pp. 34–64. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45374-9_2
Agrawal, S., Libert, B., Stehlé, D.: Fully secure functional encryption for inner products, from standard assumptions. In: Robshaw, M., Katz, J. (eds.) CRYPTO 2016. LNCS, vol. 9816, pp. 333–362. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-53015-3_12
Ananth, P., Sahai, A.: Projective arithmetic functional encryption and indistinguishability obfuscation from degree-5 multilinear maps. In: Coron, J.-S., Nielsen, J.B. (eds.) EUROCRYPT 2017. LNCS, vol. 10210, pp. 152–181. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56620-7_6
Bell, J.H., Bonawitz, K.A., Gascón, A., Lepoint, T., Raykova, M.: Secure single-server aggregation with (poly)logarithmic overhead. In: Ligatti J., Ou X., Katz J., Vigna G. (eds.) ACM CCS 2020, pp. 1253–1269. ACM Press (2020)
Benhamouda, F., Bourse, F., Lipmaa, H.: CCA-secure inner-product functional encryption from projective hash functions. In: Fehr, S. (ed.) PKC 2017. LNCS, vol. 10175, pp. 36–66. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-662-54388-7_2
Baltico, C.E.Z., Catalano, D., Fiore, D., Gay, R.: Practical functional encryption for quadratic functions with applications to predicate encryption. In: Katz, J., Shacham, H. (eds.) CRYPTO 2017. LNCS, vol. 10401, pp. 67–98. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63688-7_3
Badrinarayanan, S., Gupta, D., Jain, A., Sahai, A.: Multi-input functional encryption for unbounded arity functions. In: Iwata, T., Cheon, J.H. (eds.) ASIACRYPT 2015. LNCS, vol. 9452, pp. 27–51. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48797-6_2
Bonawitz, K.A., et al.: Practical secure aggregation for privacy-preserving machine learning. In: Thuraisingham, B., Evans, D., Malkin, T., Xu, D. (eds.) ACM CCS 2017, pp. 1175–1191. ACM Press (2017)
Brakerski, Z., Komargodski, I., Segev, G.: Multi-input functional encryption in the private-key setting: stronger security from weaker assumptions. In: Fischlin, M., Coron, J.-S. (eds.) EUROCRYPT 2016, Part II. LNCS, vol. 9666, pp. 852–880. Springer, Heidelberg (2016)
Boneh, D., Sahai, A., Waters, B.: Functional encryption: definitions and challenges. In: Ishai, Y. (ed.) TCC 2011. LNCS, vol. 6597, pp. 253–273. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19571-6_16
Chotard, J., Dufour-Sans, E., Gay, R., Phan, D.H., Pointcheval, D.: Dynamic decentralized functional encryption. In: Micciancio, D., Ristenpart, T. (eds.) CRYPTO 2020. LNCS, vol. 12170, pp. 747–775. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-56784-2_25
Castagnos, G., Laguillaumie, F., Tucker, I.: Practical fully secure unrestricted inner product functional encryption modulo p. In: Peyrin, T., Galbraith, S. (eds.) ASIACRYPT 2018, Part II. LNCS, vol. 11273, pp. 733–764. Springer, Cham (2018)
Chotard, J., Dufour Sans, E., Gay, R., Phan, D.H., Pointcheval, D.: Decentralized multi-client functional encryption for inner product. In: Peyrin, T., Galbraith, S. (eds.) ASIACRYPT 2018. LNCS, vol. 11273, pp. 703–732. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03329-3_24
Fan, X., Tang, Q.: Making public key functional encryption function private, distributively. In: Abdalla, M., Dahab, R. (eds.) PKC 2018. LNCS, vol. 10770, pp. 218–244. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76581-5_8
Gay, R.: A new paradigm for public-key functional encryption for degree-2 polynomials. In: Kiayias, A., Kohlweiss, M., Wallden, P., Zikas, V. (eds.) PKC 2020. LNCS, vol. 12110, pp. 95–120. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45374-9_4
Goldwasser, S., Gordon, S.D., Goyal, V., Jain, A., Katz, J., Liu, F.-H., Sahai, A., Shi, E., Zhou, H.-S.: Multi-input functional encryption. In: Nguyen, P.Q., Oswald, E. (eds.) EUROCRYPT 2014. LNCS, vol. 8441, pp. 578–602. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-55220-5_32
Garg, S., Gentry, C., Halevi, S.: Candidate multilinear maps from ideal lattices. In: Johansson, T., Nguyen, P.Q. (eds.) EUROCRYPT 2013. LNCS, vol. 7881, pp. 1–17. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38348-9_1
Garg, S., Gentry, C., Halevi, S., Raykova, M., Sahai, A., Waters, B.: Candidate indistinguishability obfuscation and functional encryption for all circuits. In: FOCS 2013, pp. 40–49. IEEE Computer Society Press (2013)
Goyal, V., Jain, A., O’Neill, A.: Multi-input Functional Encryption with Unbounded-Message Security. In: Cheon, J.H., Takagi, T. (eds.) ASIACRYPT 2016. LNCS, vol. 10032, pp. 531–556. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-53890-6_18
Lin, H., Tessaro, S.: Indistinguishability obfuscation from trilinear maps and block-wise local PRGs. In: Katz, J., Shacham, H. (eds.) CRYPTO 2017. LNCS, vol. 10401, pp. 630–660. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63688-7_21
Libert, B., Ţiţiu, R.: Multi-client functional encryption for linear functions in the standard model from LWE. In: Galbraith, S.D., Moriai, S. (eds.) ASIACRYPT 2019. LNCS, vol. 11923, pp. 520–551. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-34618-8_18
Mera, J.M.B., Karmakar, A., Marc, T., Soleimanian, A.: Efficient lattice-based inner-product functional encryption. In: Hanaoka, G., Shikata, J., Watanabe, Y. (eds.) PKC 2022, Part II. LNCS, vol. 13178, pp. 163–193. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-97131-1_6
Marc, T., Stopar, M., Hartman, J., Bizjak, M., Modic, J.: Privacy-enhanced machine learning with functional encryption. In: Sako, K., Schneider, S., Ryan, P.Y.A. (eds.) ESORICS 2019. LNCS, vol. 11735, pp. 3–21. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29959-0_1
Nguyen, K., Phan, D.H., Pointcheval, D.: Multi-client functional encryption with fine-grained access control. In: Agrawal, S., Lin, D. (eds.) ASIACRYPT 2022, Part I. LNCS, vol. 13791, pp. 95–125. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-22963-3_4
Rogaway, P.: The Moral Character of Cryptographic Work. Cryptology ePrint Archive. Report 2015/1162 (2015). http://eprint.iacr.org/2015/1162
Ryffel, T., Pointcheval, D., Bach, F., Dufour-Sans, E., Gay, R.: Partially encrypted deep learning using functional encryption. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E.B., Garnett, R. (eds.) NeurIPS 2019, pp. 4519–4530. Canada (2019)
Wee, H.: Functional encryption for quadratic functions from k-Lin, revisited. In: Pass, R., Pietrzak, K. (eds.) TCC 2020. LNCS, vol. 12550, pp. 210–228. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64375-1_8
Acknowledgements
We would like to thank the anonymous reviewers for their invaluable comments. This work is supported by the National Natural Science Foundation of China (Nos. 61960206014, 62121001 and 62172434), and China 111 Project (No. B16037). Willy Susilo was partially supported by the Australian Research Council (ARC) Discovery project (DP200100144) and the Australian Laureate Fellowship (FL230100033). Fuchun Guo was partially supported by the Australian Future Fellowship (FT220100046).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 International Association for Cryptologic Research
About this paper
Cite this paper
Li, Y., Wei, J., Guo, F., Susilo, W., Chen, X. (2023). Robust Decentralized Multi-client Functional Encryption: Motivation, Definition, and Inner-Product Constructions. In: Guo, J., Steinfeld, R. (eds) Advances in Cryptology – ASIACRYPT 2023. ASIACRYPT 2023. Lecture Notes in Computer Science, vol 14442. Springer, Singapore. https://doi.org/10.1007/978-981-99-8733-7_5
Download citation
DOI: https://doi.org/10.1007/978-981-99-8733-7_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8732-0
Online ISBN: 978-981-99-8733-7
eBook Packages: Computer ScienceComputer Science (R0)