Private function evaluation by local two-party computation

Stefan Rass,Monika Brodbeck,Peter Schartner

doi:10.1186/s13635-015-0025-9

Stefan Rass, Monika Brodbeck + Show 1 more

Open Access

https://doi.org/10.1186/s13635-015-0025-9

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Abstract

Information processing services are becoming increasingly pervasive, such as is demonstrated by the Internet of Things or smart grids. Given the importance that these services have reached in our daily life, the demand for security and privacy in the data processing appears equally large. Preserving the privacy of data during its processing is a challenging issue that has led to ingenious new cryptographic solutions, such as fully homomorphic encryption (to name only one). An optimal cryptographic support for private data processing must in any case be scalable and lightweight. To this end, we discuss the application of standard (off-the-shelf) cryptography to enable the computation of any function under permanent disguise (encryption). Using a local form of multiparty computation (essentially in a non-distributed fashion), we show how to execute any data processing algorithm in complete privacy. Our solution can, for example, be used with smart grid equipment, when small hardware security modules are locally available (such as in smart meters).

Highlights

Smart metering, Internet of Things, and Internet applications increasingly require data distribution over a very large scale—often spanning across nations and in some cases continents
The idea of involving multiple instances towards secure computation of functions is itself a third branch of research related to private function evaluation, known as multiparty computation (MPC) [12]
We leave this third alternative aside here for space reasons and for the sake of describing a fourth and entirely generic construction, which is based on standard encryption and works without interaction over distributed entities, which imposes overhead by additional network traffic

Summary

Introduction

Internet of Things, and Internet applications increasingly require data distribution over a very large scale—often spanning across nations and in some cases continents. In many of these applications, the dissemination of data could be made more efficient and effective by ensuring that only relevant data is delivered to interested consumers by taking into account the information content of some of its meta-information, such as the location. For applications that are not willing to reveal information or meta-information to “un-trusted” services (clouds, etc.), the infrastructure has no alternative but to rely on functional (e.g., homomorphic) encryption for secure data aggregation or dissemination without disclosure.

Secure function evaluation: the three cryptographic ways

Secure function evaluation in a local two-party setting

Conclusions

19. Imagination