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Decentralizing Inner-Product Functional Encryption

  • Michel AbdallaEmail author
  • Fabrice Benhamouda
  • Markulf Kohlweiss
  • Hendrik Waldner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11443)

Abstract

Multi-client functional encryption (MCFE) is a more flexible variant of functional encryption whose functional decryption involves multiple ciphertexts from different parties. Each party holds a different secret key and can independently and adaptively be corrupted by the adversary. We present two compilers for MCFE schemes for the inner-product functionality, both of which support encryption labels. Our first compiler transforms any scheme with a special key-derivation property into a decentralized scheme, as defined by Chotard et al. (ASIACRYPT 2018), thus allowing for a simple distributed way of generating functional decryption keys without a trusted party. Our second compiler allows to lift an unnatural restriction present in existing (decentralized) MCFE schemes, which requires the adversary to ask for a ciphertext from each party. We apply our compilers to the works of Abdalla et al. (CRYPTO 2018) and Chotard et al. (ASIACRYPT 2018) to obtain schemes with hitherto unachieved properties. From Abdalla et al., we obtain instantiations of DMCFE schemes in the standard model (from DDH, Paillier, or LWE) but without labels. From Chotard et al., we obtain a DMCFE scheme with labels still in the random oracle model, but without pairings.

Notes

Acknowledgments

This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement 780108 (FENTEC), by the ERC Project aSCEND (H2020 639554), by the French Programme d’Investissement d’Avenir under national project RISQ P141580, and by the French FUI project ANBLIC.

References

  1. 1.
    Abdalla, M., Benhamouda, F., Kolhweiss, M., Waldner, H.: Decentralizing inner-product functional encryption. Cryptology ePrint Archive, Report 2019/020 (2019). http://eprint.iacr.org/2019/020
  2. 2.
    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_33CrossRefGoogle Scholar
  3. 3.
    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, Part I. LNCS, vol. 10991, pp. 597–627. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-96884-1_20CrossRefGoogle Scholar
  4. 4.
    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, Part I. LNCS, vol. 10210, pp. 601–626. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-56620-7_21CrossRefGoogle Scholar
  5. 5.
    Agrawal, S., Libert, B., Stehlé, D.: Fully secure functional encryption for inner products, from standard assumptions. In: Robshaw, M., Katz, J. (eds.) CRYPTO 2016, Part III. LNCS, vol. 9816, pp. 333–362. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-53015-3_12CrossRefGoogle Scholar
  6. 6.
    Ananth, P., Jain, A.: Indistinguishability obfuscation from compact functional encryption. In: Gennaro, R., Robshaw, M.J.B. (eds.) CRYPTO 2015, Part I. LNCS, vol. 9215, pp. 308–326. Springer, Heidelberg (2015).  https://doi.org/10.1007/978-3-662-47989-6_15CrossRefGoogle Scholar
  7. 7.
    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, Part I. LNCS, vol. 9452, pp. 27–51. Springer, Heidelberg (2015).  https://doi.org/10.1007/978-3-662-48797-6_2CrossRefGoogle Scholar
  8. 8.
    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, Part I. LNCS, vol. 10401, pp. 67–98. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-63688-7_3CrossRefGoogle Scholar
  9. 9.
    Bellare, M., Desai, A., Jokipii, E., Rogaway, P.: A concrete security treatment of symmetric encryption. In: 38th FOCS, pp. 394–403. IEEE Computer Society Press, October 1997.  https://doi.org/10.1109/SFCS.1997.646128
  10. 10.
    Benhamouda, F., Joye, M., Libert, B.: A new framework for privacy-preserving aggregation of time-series data. ACM Trans. Inf. Syst. Secur. 18(3), 101–1021 (2016).  https://doi.org/10.1145/2873069CrossRefGoogle Scholar
  11. 11.
    Bishop, A., Jain, A., Kowalczyk, L.: Function-hiding inner product encryption. In: Iwata, T., Cheon, J.H. (eds.) ASIACRYPT 2015, Part I. LNCS, vol. 9452, pp. 470–491. Springer, Heidelberg (2015).  https://doi.org/10.1007/978-3-662-48797-6_20CrossRefGoogle Scholar
  12. 12.
    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_16CrossRefGoogle Scholar
  13. 13.
    Brakerski, Z., Komargodski, I., Segev, G.: Multi-input functional encryption in the private-key setting: stronger security from weaker assumptions. J. Cryptol. 31(2), 434–520 (2018).  https://doi.org/10.1007/s00145-017-9261-0MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Chan, T.-H.H., Shi, E., Song, D.: Privacy-preserving stream aggregation with fault tolerance. In: Keromytis, A.D. (ed.) FC 2012. LNCS, vol. 7397, pp. 200–214. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-32946-3_15CrossRefGoogle Scholar
  15. 15.
    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, Part II. LNCS, vol. 11273, pp. 703–732. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-03329-3_24CrossRefGoogle Scholar
  16. 16.
    Chotard, J., Dufour Sans, E., Gay, R., Phan, D.H., Pointcheval, D.: Multi-client functional encryption with repetition for inner product. Cryptology ePrint Archive, Report 2018/1021 (2018). http://eprint.iacr.org/2018/1021
  17. 17.
    Datta, P., Dutta, R., Mukhopadhyay, S.: Functional encryption for inner product with full function privacy. In: Cheng, C.-M., Chung, K.-M., Persiano, G., Yang, B.-Y. (eds.) PKC 2016, Part I. LNCS, vol. 9614, pp. 164–195. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-49384-7_7CrossRefGoogle Scholar
  18. 18.
    Emura, K.: Privacy-preserving aggregation of time-series data with public verifiability from simple assumptions. In: Pieprzyk, J., Suriadi, S. (eds.) ACISP 2017. LNCS, vol. 10343, pp. 193–213. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-59870-3_11CrossRefzbMATHGoogle Scholar
  19. 19.
    Fan, X., Tang, Q.: Making public key functional encryption function private, distributively. In: Abdalla, M., Dahab, R. (eds.) PKC 2018, Part II. LNCS, vol. 10770, pp. 218–244. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-76581-5_8CrossRefGoogle Scholar
  20. 20.
    Goldwasser, S., et al.: 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_32CrossRefGoogle Scholar
  21. 21.
    Joye, M., Libert, B.: A scalable scheme for privacy-preserving aggregation of time-series data. In: Sadeghi, A.-R. (ed.) FC 2013. LNCS, vol. 7859, pp. 111–125. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-39884-1_10CrossRefzbMATHGoogle Scholar
  22. 22.
    Li, Q., Cao, G.: Efficient and privacy-preserving data aggregation in mobile sensing. In: 20th IEEE International Conference on Network Protocols, ICNP, pp. 1–10. IEEE Computer Society, Austin (2012).  https://doi.org/10.1109/ICNP.2012.6459985
  23. 23.
    O’Neill, A.: Definitional issues in functional encryption. Cryptology ePrint Archive, Report 2010/556 (2010). http://eprint.iacr.org/2010/556
  24. 24.
    Sahai, A., Waters, B.R.: Fuzzy identity-based encryption. In: Cramer, R. (ed.) EUROCRYPT 2005. LNCS, vol. 3494, pp. 457–473. Springer, Heidelberg (2005).  https://doi.org/10.1007/11426639_27CrossRefGoogle Scholar
  25. 25.
    Shi, E., Chan, T.H.H., Rieffel, E.G., Chow, R., Song, D.: Privacy-preserving aggregation of time-series data. In: NDSS 2011. The Internet Society, February 2011Google Scholar

Copyright information

© International Association for Cryptologic Research 2019

Authors and Affiliations

  1. 1.DIENS, École normale supérieure, CNRS, PSL UniversityParisFrance
  2. 2.InriaParisFrance
  3. 3.IBM ResearchYorktown HeightsUSA
  4. 4.University of EdinburghEdinburghUK

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