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Labeled Homomorphic Encryption

Scalable and Privacy-Preserving Processing of Outsourced Data
  • Manuel Barbosa
  • Dario CatalanoEmail author
  • Dario Fiore
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10492)

Abstract

In privacy-preserving processing of outsourced data a Cloud server stores data provided by one or multiple data providers and then is asked to compute several functions over it. We propose an efficient methodology that solves this problem with the guarantee that a honest-but-curious Cloud learns no information about the data and the receiver learns nothing more than the results. Our main contribution is the proposal and efficient instantiation of a new cryptographic primitive called Labeled Homomorphic Encryption (labHE). The fundamental insight underlying this new primitive is that homomorphic computation can be significantly accelerated whenever the program that is being computed over the encrypted data is known to the decrypter and is not secret—previous approaches to homomorphic encryption do not allow for such a trade-off. Our realization and implementation of labHE targets computations that can be described by degree-two multivariate polynomials. As an application, we consider privacy preserving Genetic Association Studies (GAS), which require computing risk estimates from features in the human genome. Our approach allows performing GAS efficiently, non interactively and without compromising neither the privacy of patients nor potential intellectual property of test laboratories.

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Notes

Acknowledgements

The work of Dario Fiore was partially supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement 688722 (NEXTLEAP), the Spanish Ministry of Economy under project references TIN2015-70713-R (DEDETIS), RTC-2016-4930-7 (DataMantium), and under a Juan de la Cierva fellowship to Dario Fiore, and by the Madrid Regional Government under project N-Greens (ref. S2013/ICE-2731). Manuel Barbosa was funded by project “NanoSTIMA: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016”, which is financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.INESC TEC and FCUPPortoPortugal
  2. 2.University of CataniaCataniaItaly
  3. 3.IMDEA Software Institute MadridMadridSpain

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