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(F)unctional Sifting: A Privacy-Preserving Reputation System Through Multi-Input Functional Encryption

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Secure IT Systems (NordSec 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12556))

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Abstract

Functional Encryption (FE) allows users who hold a specific secret key (known as the functional key) to learn a specific function of encrypted data whilst learning nothing about the content of the underlying data. Considering this functionality and the fact that the field of FE is still in its infancy, we sought a route to apply this potent tool to solve the existing problem of designing decentralised additive reputation systems. To this end, we first built a symmetric FE scheme for the \(\ell _1\) norm of a vector space, which allows us to compute the sum of the components of an encrypted vector (i.e. the votes). Then, we utilized our construction, along with functionalities offered by Intel SGX, to design the first FE-based decentralized additive reputation system with Multi-Party Computation. While our reputation system faces certain limitations, this work is amongst the first attempts that seek to utilize FE in the solution of a real-life problem.

This work was funded by the ASCLEPIOS: Advanced Secure Cloud Encrypted Platform for Internationally Orchestrated Solutions in Healthcare Project No. 826093 EU research project.

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Notes

  1. 1.

    In the literature, this algorithm can often be found as \(\mathsf {KeyGen}(\mathbf {K}, f)\) where it outputs an \(\mathsf {FK}\) for a specific function f. This is the case, with the MIFE scheme from [1] presented in Sect. 4. In our case, we only work with one function, so we can omit the f term in the definition of the algorithm.

  2. 2.

    https://software.intel.com/en-us/sgx/sdk.

References

  1. 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

    Chapter  Google Scholar 

  2. 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

    Chapter  Google Scholar 

  3. Ananth, P., Brakerski, Z., Segev, G., Vaikuntanathan, V.: From selective to adaptive security in functional encryption. In: Gennaro, R., Robshaw, M. (eds.) CRYPTO 2015. LNCS, vol. 9216, pp. 657–677. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48000-7_32

    Chapter  MATH  Google Scholar 

  4. Badrinarayanan, S., Goyal, V., Jain, A., Sahai, A.: Verifiable functional encryption. In: Cheon, J.H., Takagi, T. (eds.) ASIACRYPT 2016. LNCS, vol. 10032, pp. 557–587. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-53890-6_19

    Chapter  Google Scholar 

  5. Bakas, A., Michalas, A.: Power range: forward private multi-client symmetric searchable encryption with range queries support. In: 2020 IEEE Symposium on Computers and Communications (ISCC), pp. 1–7 (2020)

    Google Scholar 

  6. Bakas, A., Michalas, A.: Multi-client symmetric searchable encryption with forward privacy. Cryptology ePrint Archive, Report 2019/813 (2019)

    Google Scholar 

  7. Bakas, A., Michalas, A.: Multi-input functional encryption: efficient applications from symmetric primitives. In: Proceedings of the 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2020) (2020)

    Google Scholar 

  8. 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)

    Article  MathSciNet  Google Scholar 

  9. Costan, V., Devadas, S.: Intel SGX explained. IACR Cryptol. ePrint Arch. 2016(086), 1–118 (2016)

    Google Scholar 

  10. Dimitriou, T., Michalas, A.: Multi-party trust computation in decentralized environments in the presence of malicious adversaries. Ad Hoc Netw. 15, 53–66 (2014)

    Article  Google Scholar 

  11. Dolev, D., Yao, A.C.: On the security of public key protocols. IEEE Trans. Inf. Theory 29(2), 198–208 (1983)

    Article  MathSciNet  Google Scholar 

  12. 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_32

    Chapter  Google Scholar 

  13. Goldwasser, S., Kalai, Y.T., Popa, R.A., Vaikuntanathan, V., Zeldovich, N.: How to run turing machines on encrypted data. In: Canetti, R., Garay, J.A. (eds.) CRYPTO 2013. LNCS, vol. 8043, pp. 536–553. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40084-1_30

    Chapter  Google Scholar 

  14. Gorbunov, S., Vaikuntanathan, V., Wee, H.: Functional encryption with bounded collusions via multi-party computation. In: Safavi-Naini, R., Canetti, R. (eds.) CRYPTO 2012. LNCS, vol. 7417, pp. 162–179. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32009-5_11

    Chapter  Google Scholar 

  15. Hasan, O., Brunie, L., Bertino, E., Shang, N.: A decentralized privacy preserving reputation protocol for the malicious adversarial model. IEEE Trans. Inf. Forensics Secur. 8(6), 949–962 (2013)

    Article  Google Scholar 

  16. Pavlov, E., Rosenschein, J.S., Topol, Z.: Supporting privacy in decentralized additive reputation systems. In: Jensen, C., Poslad, S., Dimitrakos, T. (eds.) iTrust 2004. LNCS, vol. 2995, pp. 108–119. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24747-0_9

    Chapter  Google Scholar 

  17. Sans, E.D., Gay, R., Pointcheval, D.: Reading in the dark: classifying encrypted digits with functional encryption. IACR Cryptology ePrint Archive 2018

    Google Scholar 

  18. Waters, B.: A punctured programming approach to adaptively secure functional encryption. In: Gennaro, R., Robshaw, M. (eds.) CRYPTO 2015. LNCS, vol. 9216, pp. 678–697. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48000-7_33

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Tampere University of Technology, Tampere, Finland

    Alexandros Bakas & Antonis Michalas

  2. Univeristy of Westminster, London, UK

    Amjad Ullah

Authors
  1. Alexandros Bakas
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  2. Antonis Michalas
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  3. Amjad Ullah
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Correspondence to Alexandros Bakas .

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Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Mikael Asplund

  2. Linköping University, Linköping, Sweden

    Simin Nadjm-Tehrani

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Bakas, A., Michalas, A., Ullah, A. (2021). (F)unctional Sifting: A Privacy-Preserving Reputation System Through Multi-Input Functional Encryption. In: Asplund, M., Nadjm-Tehrani, S. (eds) Secure IT Systems. NordSec 2020. Lecture Notes in Computer Science(), vol 12556. Springer, Cham. https://doi.org/10.1007/978-3-030-70852-8_7

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  • DOI: https://doi.org/10.1007/978-3-030-70852-8_7

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