Abstract
Homomorphic encryption allows computation over encrypted data and can be used for delegating computation: data providers encrypt their data and send them to an aggregator, and then the aggregator performs computation for a receiver with the data kept secret. However, since the aggregator is merely the third party, it may be malicious, and particularly may submit a result of incorrect aggregation to the receiver. Ohara et al. (APKC2014) studied secure aggregation of time-series data while enabling the correctness of aggregation to be verified. However, they only provided a concrete construction in the smart metering system and only gave an intuitive argument of security. In this paper, we give general syntax of their scheme as verifiable homomorphic encryption (VHE) and introduce formal security definitions. Further, we formally prove that Ohara et al.’s VHE scheme satisfies our proposed security definitions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Abe, M., Fuchsbauer, G., Groth, J., Haralambiev, K., Ohkubo, M.: Structure-preserving signatures and commitments to group elements. In: Rabin, T. (ed.) CRYPTO 2010. LNCS, vol. 6223, pp. 209–236. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14623-7_12
El-Yahyaoui, A., El Kettani, M.D.E.C.: A verifiable fully homomorphic encryption scheme to secure big data in cloud computing. In: WINCOM, pp 1–5. IEEE (2017)
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_11
Fiore, D., Gennaro, R., Pastro, V.: Efficiently verifiable computation on encrypted data. In: ACM Conference on Computer and Communications Security, pp. 844–855. ACM (2014)
Lai, J., Deng, R.H., Pang, H., Weng, J.: Verifiable computation on outsourced encrypted data. In: Kutyłowski, M., Vaidya, J. (eds.) ESORICS 2014. LNCS, vol. 8712, pp. 273–291. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11203-9_16
Leontiadis, I., Elkhiyaoui, K., Önen, M., Molva, R.: PUDA – privacy and unforgeability for data aggregation. In: Reiter, M., Naccache, D. (eds.) CANS 2015. LNCS, vol. 9476, pp. 3–18. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26823-1_1
Ohara, K., Sakai, Y., Yoshida, F., Iwamoto, M., Ohta, K.: Privacy-preserving smart metering with verifiability for both billing and energy management. In: AsiaPKC@AsiaCCS, pp. 23–32. ACM (2014)
Tran, N.H., Pang, H., Deng, R.H.: Efficient verifiable computation of linear and quadratic functions over encrypted data. In: AsiaCCS, pp. 605–616. ACM (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Yasuda, S., Koseki, Y., Sakai, Y., Kitagawa, F., Kawai, Y., Hanaoka, G. (2018). Formal Treatment of Verifiable Privacy-Preserving Data-Aggregation Protocols. In: Baek, J., Susilo, W., Kim, J. (eds) Provable Security. ProvSec 2018. Lecture Notes in Computer Science(), vol 11192. Springer, Cham. https://doi.org/10.1007/978-3-030-01446-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-01446-9_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01445-2
Online ISBN: 978-3-030-01446-9
eBook Packages: Computer ScienceComputer Science (R0)