Manual for Using Homomorphic Encryption for Bioinformatics

Dowlin, Nathan; Gilad-Bachrach, Ran; Laine, Kim; Lauter, Kristin; Naehrig, Michael; Wernsing, John

doi:10.1109/jproc.2016.2622218

Cited by 100 publications

(111 citation statements)

References 20 publications

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“…We adopt the MovieLens 100k dataset where M = 1682 and set (|F u |, |T u |) = (70, 10). We use the Microsoft SEAL library [12] based on YASHE scheme. The timing information of the SEAL lib is Enc (42 ms), Dec (41 ms), Eval(·, , ) (305 ms), Eval(+, , ) (85 µs).…”

Section: A Asymptotic Analysismentioning

confidence: 99%

Privacy-Preserving Friendship-Based Recommender Systems

Tang

Wang

2018

IEEE Trans. Dependable and Secure Comput.

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Privacy-preserving recommender systems have been an active research topic for many years. However, until today, it is still a challenge to design an efficient solution without involving a fully trusted third party or multiple semitrusted third parties. The key obstacle is the large underlying user populations (i.e. huge input size) in the systems. In this paper, we revisit the concept of friendship-based recommender systems, proposed by Jeckmans et al. and Tang and Wang. These solutions are very promising because recommendations are computed based on inputs from a very small subset of the overall user population (precisely, a user's friends and some randomly chosen strangers). We first clarify the single prediction protocol and Top-n protocol by Tang and Wang, by correcting some flaws and improving the efficiency of the single prediction protocol. We then design a decentralized single protocol by getting rid of the semi-honest service provider. In order to validate the designed protocols, we crawl Twitter and construct two datasets (FMT and 10-FMT) which are equipped with auxiliary friendship information. Based on 10-FMT and MovieLens 100k dataset with simulated friendships, we show that even if our protocols use a very small subset of the datasets, their accuracy can still be equal to or better than some baseline algorithm. Based on these datasets, we further demonstrate that the outputs of our protocols leak very small amount of information of the inputs, and the leakage decreases when the input size increases. We finally show that he single prediction protocol is quite efficient but the Top-n is not. However, we observe that the efficiency of the Top-n protocol can be dramatically improved if we slightly relax the desired security guarantee.

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Section: A Asymptotic Analysismentioning

confidence: 99%

Privacy-Preserving Friendship-Based Recommender Systems

Tang

Wang

2018

IEEE Trans. Dependable and Secure Comput.

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“…The first encodes the complex number as a pair of real numbers and therefore holds the encrypted complex number as the encryption of two real numbers. The real numbers would then be encrypted using the methods suggested in [5,6] to encode fixed-point numbers. A major downside of this methodology is that to add two encrypted complex numbers requires two homomorphic additions, and to multiply two encrypted complex numbers requires four homomorphic multiplications.…”

Section: Encoding Approximations To Arbitrary Complex Numbersmentioning

confidence: 99%

“…This need to process real and complex arithmetic homomorphically has led some authors to propose encoding methods for such numbers [4][5][6] in the context of encryption schemes based on Ring-LWE. Such schemes are typified by the BGV scheme [3].…”

Section: Introductionmentioning

confidence: 99%

“…Writing R p and R q for the ring reduced modulo p and q respectively, we have that R p represents the space of all possible plaintexts and R 2 q is the ciphertext space. The first methodology [5,6] to perform homomorphic operations on real numbers (and hence complex numbers) used a fixed point representation based on the polynomial expansion of the real number with respect to some "base". This polynomial is then embedded into the plaintext space, and homomorphic operations on the polynomials map into homomorphic operations on the underlying fixed point number.…”

Section: Introductionmentioning

confidence: 99%

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Faster Homomorphic Evaluation of Discrete Fourier Transforms

Costache¹,

Smart²,

Vivek

2017

Financial Cryptography and Data Security

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Abstract. We present a methodology to achieve low latency homomorphic operations on approximations to complex numbers, by encoding a complex number as an evaluation of a polynomial at a root of unity. We then use this encoding to evaluate a Discrete Fourier Transform (DFT) on data which has been encrypted using a Somewhat Homomorphic Encryption (SHE) scheme, with up to three orders of magnitude improvement in latency over previous methods. We are also able to deal with much larger input sizes than previous methods. Due to the fact that the entire DFT algorithm is an algebraic operation over the underlying ring of the SHE scheme (for a suitably chosen ring), our method for the DFT utilizes exact arithmetic over the complex numbers, as opposed to approximations.

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“…The paper's abstract [40] explains how this technology could be used in the cloud to process encrypted data without needing the decryption keys: "The encryption ensures that the data remains confidential since the cloud does not have access to the keys needed to decrypt it. Nevertheless, we will show that the cloud service is capable of applying the neural network to the encrypted data to make encrypted predictions, and also return them in encrypted form.…”

Section: ) Information Dispersal Algorithm Techniquementioning

confidence: 99%

New mechanism for Cloud Computing Storage Security

Mrabti¹,

Ammari²,

Kalam³

et al. 2016

ijacsa

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Abstract-Cloud computing, often referred to as simply the cloud, appears as an emerging computing paradigm which promises to radically change the way computer applications and services are constructed, delivered, managed and finally guaranteed as dynamic computing environments for end users. The cloud is the delivery of on-demand computing resourceseverything from applications to data centers -over the Internet on a pay-for-use basis. The revolution of cloud computing has provided opportunities for research in all aspects of cloud computing. Despite the big progress in cloud computing technologies, funding concerns in cloud, security may limit a broader adoption. This paper presents a technique to tolerate both accidental and intentional faults, which is fragmentation-redundancy-scattering (FRS). The possibility to use the FRS technique as an intrusion tolerance one is investigated for providing secure and dependable storage in the cloud environment. Also a cloud computing security (CCS) based on the FRS technique is proposed to explore how this proposal can then be used via several scenarios. To demonstrate the robustness of the proposal, we formalize our design and we carry out a security as well as performance evaluations of the approach and we compare it with the classical model. The paper concludes by strongly suggesting future research proposals for the CCS framework.

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Manual for Using Homomorphic Encryption for Bioinformatics

Cited by 100 publications

References 20 publications

Privacy-Preserving Friendship-Based Recommender Systems

Privacy-Preserving Friendship-Based Recommender Systems

Faster Homomorphic Evaluation of Discrete Fourier Transforms

New mechanism for Cloud Computing Storage Security

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