Recent Advances in Homomorphic Encryption: A Possible Future for Signal Processing in the Encrypted Domain

Aguilar-Melchor, Carlos; Fau, Simon; Fontaine, Caroline; Gogniat, Guy; Sirdey, Renaud

doi:10.1109/msp.2012.2230219

Cited by 131 publications

(47 citation statements)

References 31 publications

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“…In particular, in the work of [7], the same server requests would generate the same tags. For a simple and general overview of homomorphic encryption concepts the reader is encouraged to read [1], [5], [28].…”

Section: Related Workmentioning

confidence: 99%

“…For example, if the application in hand needs many multiplication operations to be done to match one entry as in (1), this may lead to the rapid growth of the noise in the Ctxt to the limit that it may not be decrypted correctly. On the other hand, as discussed in Section 3.1.1, in the case of non-matching items, the result will have much less noise.…”

Section: Optimizationmentioning

confidence: 99%

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SHIELD: Scalable Homomorphic Implementation of Encrypted Data-Classifiers

Khedr

Gulak

Vaikuntanathan

2016

IEEE Trans. Comput.

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Abstract-Homomorphic encryption (HE) systems enable computations on encrypted data, without decrypting and without knowledge of the secret key. In this work, we describe an optimized Ring Learning With Errors (RLWE) based implementation of a variant of the HE system recently proposed by Gentry, Sahai and Waters (GSW). Although this system was widely believed to be less efficient than its contemporaries, we demonstrate quite the opposite behavior for a large class of applications. We first highlight and carefully exploit the algebraic features of the system to achieve significant speedup over the state-of-the-art HE implementation, namely the IBM homomorphic encryption library (HElib). We introduce several optimizations on top of our HE implementation, and use the resulting scheme to construct a homomorphic Bayesian spam filter, secure multiple keyword search, and a homomorphic evaluator for binary decision trees. Our results show a factor of 10× improvement in performance (under the same security settings and CPU platforms) compared to IBM HElib for these applications. Our system is built to be easily portable to GPUs (unlike IBM HElib) which results in an additional speedup of up to a factor of 103.5× to offer an overall speedup of 1035×.

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Section: Related Workmentioning

confidence: 99%

Section: Optimizationmentioning

confidence: 99%

SHIELD: Scalable Homomorphic Implementation of Encrypted Data-Classifiers

Khedr

Gulak

Vaikuntanathan

2016

IEEE Trans. Comput.

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“…To our knowledge, the idea of sorting an array of encrypted integers was first discussed by Carlos et al [3]. Here authors explicitly presented control flow of Bubble sort algorithm that can sort encrypted data.…”

Section: B Related Workmentioning

confidence: 99%

Updates on Sorting of Fully Homomorphic Encrypted Data

Emmadi

Gauravaram

Narumanchi

et al. 2015

2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)

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Abstract-In this paper, we show implementation results of various algorithms that sort data encrypted with Fully Homomorphic Encryption scheme based on Integers. We analyze the complexities of sorting algorithms over encrypted data by considering Bubble Sort, Insertion Sort, Bitonic Sort and OddEven Merge sort. Our complexity analysis together with implementation results show that Odd-Even Merge Sort has better performance than the other sorting techniques. We observe that complexity of sorting in homomorphic domain will always have worst case complexity independent of the nature of input. In addition, we show that combining different sorting algorithms to sort encrypted data does not give any performance gain when compared to the application of sorting algorithms individually.

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“…In any case, however, we can use a standard compiler to create the processing software and can "directly" run this code on encrypted data. Note that this is an essential difference to alternative approaches (like GC or FHE), which require specialpurpose compilers [32][33][34] and designated code/circuit generation [35] that is specific for a particular function. Unlike this, the computing platform sketched here is "general purpose".…”

Section: Securitymentioning

confidence: 99%

Private function evaluation by local two-party computation

Raß

Schartner

Brodbeck

2015

EURASIP J. on Info. Security

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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).

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Recent Advances in Homomorphic Encryption: A Possible Future for Signal Processing in the Encrypted Domain

Cited by 131 publications

References 31 publications

SHIELD: Scalable Homomorphic Implementation of Encrypted Data-Classifiers

SHIELD: Scalable Homomorphic Implementation of Encrypted Data-Classifiers

Updates on Sorting of Fully Homomorphic Encrypted Data

Private function evaluation by local two-party computation

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