Simple Functional Encryption Schemes for Inner Products

2015 ◽  
pp. 733-751 ◽  
Author(s):  
Michel Abdalla ◽  
Florian Bourse ◽  
Angelo De Caro ◽  
David Pointcheval
Keyword(s):  
Functional Encryption ◽  
Inner Products ◽  
Encryption Schemes

scholarly journals Universal Composability Notion for Functional Encryption Schemes

2013 ◽  
Vol 18 (3) ◽  
pp. 17-26 ◽  
Author(s):  
Rifki Sadikin ◽  
YoungHo Park ◽  
KilHoum Park ◽  
SangJae Moon
Keyword(s):  
Functional Encryption ◽  
Universal Composability ◽  
Encryption Schemes

scholarly journals Efficient Function-Hiding Functional Encryption: From Inner-Products to Orthogonality

2019 ◽  
pp. 127-148 ◽  
Author(s):  
Manuel Barbosa ◽  
Dario Catalano ◽  
Azam Soleimanian ◽  
Bogdan Warinschi
Keyword(s):  
Functional Encryption ◽  
Inner Products

scholarly journals Two Improved Homomorphic Techniques Used in Functional Encryption Schemes

2018 ◽  
Vol 173 ◽  
pp. 03085
Author(s):  
Chengbo Xu ◽  
Shuying Yang
Keyword(s):  
Functional Encryption ◽  
Encryption Schemes

In this paper, we analyze the key homomorphic technique used in constructions of functional encryption schemes and point out its weakness in efficiency. Based on this, we propose two improved homomorphic techniques and show their advantages and weaknesses through the method of comparison.

scholarly journals Illuminating the Dark or how to recover what should not be seen in FE-based classifiers

2020 ◽  
Vol 2020 (2) ◽  
pp. 5-23
Author(s):  
Sergiu Carpov ◽  
Caroline Fontaine ◽  
Damien Ligier ◽  
Renaud Sirdey
Keyword(s):  
Data Privacy ◽  
Information Leakage ◽  
Use Cases ◽  
Security And Privacy ◽  
Functional Encryption ◽  
Cryptographic Primitives ◽  
Encrypted Data ◽  
Current State ◽  
Encryption Schemes ◽  
Privacy Issues

AbstractClassification algorithms/tools become more and more powerful and pervasive. Yet, for some use cases, it is necessary to be able to protect data privacy while benefiting from the functionalities they provide. Among the tools that may be used to ensure such privacy, we are focusing in this paper on functional encryption. These relatively new cryptographic primitives enable the evaluation of functions over encrypted inputs, outputting cleartext results. Theoretically, this property makes them well-suited to process classification over encrypted data in a privacy by design’ rationale, enabling to perform the classification algorithm over encrypted inputs (i.e. without knowing the inputs) while only getting the input classes as a result in the clear.In this paper, we study the security and privacy issues of classifiers using today practical functional encryption schemes. We provide an analysis of the information leakage about the input data that are processed in the encrypted domain with state-of-the-art functional encryption schemes. This study, based on experiments ran on MNIST and Census Income datasets, shows that neural networks are able to partially recover information that should have been kept secret. Hence, great care should be taken when using the currently available functional encryption schemes to build privacy-preserving classification services. It should be emphasized that this work does not attack the cryptographic security of functional encryption schemes, it rather warns the community against the fact that they should be used with caution for some use cases and that the current state-ofthe-art may lead to some operational weaknesses that could be mitigated in the future once more powerful functional encryption schemes are available.

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