7,234 research outputs found
New techniques for Multi-value input Homomorphic Evaluation and Applications
In this paper, we propose a new technique to perform several homomorphic operations in one bootstrapping call over a multi-value plaintext space. Our construction relies on the FHEW-based gate bootstrapping; we analyze its structure and propose a strategy we call multi-value bootstrapping which allows to bootstrap an arbitrary function in an efficient way.
The security of our scheme relies on the LWE assumption over the torus. We give three possible applications: we first describe how to efficiently evaluate an arbitrary boolean function (LUT) and combine LUTs in circuits. We also explain how to apply our procedure to optimize the circuit bootstrapping from (Asiacrypt\u272017) which allows to compose circuits in a leveled mode. And we finally present a simple method which makes use of the multi-value bootstrapping to evaluate a encrypted neural network.
We have implemented the proposed method and were able to evaluate an arbitrary 6-to-6 LUTs under 1.6 seconds. Our implementation is based on the TFHE library but can be easily integrated into other homomorphic libraries based on the same structure, such as FHEW (Eurocrypt\u272015). The number of LUT outputs does not influence the execution time by a lot, e.g. evaluation of additional 128 outputs on the same 6 input bits takes only 0.05 more seconds
Conditionals in Homomorphic Encryption and Machine Learning Applications
Homomorphic encryption aims at allowing computations on encrypted data
without decryption other than that of the final result. This could provide an
elegant solution to the issue of privacy preservation in data-based
applications, such as those using machine learning, but several open issues
hamper this plan. In this work we assess the possibility for homomorphic
encryption to fully implement its program without relying on other techniques,
such as multiparty computation (SMPC), which may be impossible in many use
cases (for instance due to the high level of communication required). We
proceed in two steps: i) on the basis of the structured program theorem
(Bohm-Jacopini theorem) we identify the relevant minimal set of operations
homomorphic encryption must be able to perform to implement any algorithm; and
ii) we analyse the possibility to solve -- and propose an implementation for --
the most fundamentally relevant issue as it emerges from our analysis, that is,
the implementation of conditionals (requiring comparison and selection/jump
operations). We show how this issue clashes with the fundamental requirements
of homomorphic encryption and could represent a drawback for its use as a
complete solution for privacy preservation in data-based applications, in
particular machine learning ones. Our approach for comparisons is novel and
entirely embedded in homomorphic encryption, while previous studies relied on
other techniques, such as SMPC, demanding high level of communication among
parties, and decryption of intermediate results from data-owners. Our protocol
is also provably safe (sharing the same safety as the homomorphic encryption
schemes), differently from other techniques such as
Order-Preserving/Revealing-Encryption (OPE/ORE).Comment: 14 pages, 1 figure, corrected typos, added introductory pedagogical
section on polynomial approximatio
A Survey on Homomorphic Encryption Schemes: Theory and Implementation
Legacy encryption systems depend on sharing a key (public or private) among
the peers involved in exchanging an encrypted message. However, this approach
poses privacy concerns. Especially with popular cloud services, the control
over the privacy of the sensitive data is lost. Even when the keys are not
shared, the encrypted material is shared with a third party that does not
necessarily need to access the content. Moreover, untrusted servers, providers,
and cloud operators can keep identifying elements of users long after users end
the relationship with the services. Indeed, Homomorphic Encryption (HE), a
special kind of encryption scheme, can address these concerns as it allows any
third party to operate on the encrypted data without decrypting it in advance.
Although this extremely useful feature of the HE scheme has been known for over
30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE)
scheme, which allows any computable function to perform on the encrypted data,
was introduced by Craig Gentry in 2009. Even though this was a major
achievement, different implementations so far demonstrated that FHE still needs
to be improved significantly to be practical on every platform. First, we
present the basics of HE and the details of the well-known Partially
Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which
are important pillars of achieving FHE. Then, the main FHE families, which have
become the base for the other follow-up FHE schemes are presented. Furthermore,
the implementations and recent improvements in Gentry-type FHE schemes are also
surveyed. Finally, further research directions are discussed. This survey is
intended to give a clear knowledge and foundation to researchers and
practitioners interested in knowing, applying, as well as extending the state
of the art HE, PHE, SWHE, and FHE systems.Comment: - Updated. (October 6, 2017) - This paper is an early draft of the
survey that is being submitted to ACM CSUR and has been uploaded to arXiv for
feedback from stakeholder
Towards the AlexNet Moment for Homomorphic Encryption: HCNN, theFirst Homomorphic CNN on Encrypted Data with GPUs
Deep Learning as a Service (DLaaS) stands as a promising solution for
cloud-based inference applications. In this setting, the cloud has a
pre-learned model whereas the user has samples on which she wants to run the
model. The biggest concern with DLaaS is user privacy if the input samples are
sensitive data. We provide here an efficient privacy-preserving system by
employing high-end technologies such as Fully Homomorphic Encryption (FHE),
Convolutional Neural Networks (CNNs) and Graphics Processing Units (GPUs). FHE,
with its widely-known feature of computing on encrypted data, empowers a wide
range of privacy-concerned applications. This comes at high cost as it requires
enormous computing power. In this paper, we show how to accelerate the
performance of running CNNs on encrypted data with GPUs. We evaluated two CNNs
to classify homomorphically the MNIST and CIFAR-10 datasets. Our solution
achieved a sufficient security level (> 80 bit) and reasonable classification
accuracy (99%) and (77.55%) for MNIST and CIFAR-10, respectively. In terms of
latency, we could classify an image in 5.16 seconds and 304.43 seconds for
MNIST and CIFAR-10, respectively. Our system can also classify a batch of
images (> 8,000) without extra overhead
Secret charing vs. encryption-based techniques for privacy preserving data mining
Privacy preserving querying and data publishing has been studied in the context of statistical databases and statistical disclosure control. Recently, large-scale data collection and integration efforts increased privacy concerns which motivated data mining researchers to investigate privacy implications of data mining and how data mining can be performed without violating privacy. In this paper, we first provide an overview of privacy preserving data mining focusing on distributed data sources, then we compare two technologies used in privacy preserving data mining. The first technology is encryption based, and it is used in earlier approaches. The second technology is secret-sharing which is recently being considered as a more efficient approach
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