Using fully homomorphic encryption scheme, we construct fully homomorphic encryption scheme FHE4GT that can homomorphically compute an encryption of the greaterthan bit that indicates 𝑥 > 𝑥 ′ or not, given two ciphertexts 𝑐 and 𝑐 ′ of 𝑥 and 𝑥 ′, respectively, without knowing the secret key. Then, we construct homomorphic classifier homClassify that can homomorphically classify a given encrypted data without decrypting it, using machine learned parameters. Read More
Tag Archives: Homomorphic Encryption
Crypto-nets: Neural Networks over encrypted data
The problem we address is the following: how can a user employ a predictive model that is held by a third party, without compromising private information. For example, a hospital may wish to use a cloud service to predict the readmission risk of a patient. However, due to regulations, the patient’s medical files cannot be revealed. The goal is to make an inference using the model, without jeopardizing the accuracy of the prediction or the privacy of the data.
To achieve high accuracy, we use neural networks, which have been shown to outperform other learning models for many tasks. To achieve the privacy requirements, we use homomorphic encryption in the following protocol: the data owner encrypts the data and sends the ciphertexts to the third party to obtain a prediction from a trained model. The model operates on these ciphertexts and sends back the encrypted prediction. In this protocol, not only the data remains private, even the values predicted are available only to the data owner.
Using homomorphic encryption and modifications to the activation functions and training algorithms of neural networks, we present crypto-nets and prove that they can be constructed and may be feasible. This method paves the way to build a secure cloud-based neural network prediction services without invading users’ privacy. Read More
Conditionals in Homomorphic Encryption and Machine Learning Applications
Homomorphic encryption has the purpose to allow computations on encrypted data, without the need for decryption other than that of the final result. This could provide an elegant solution to the problem of privacy preservation in data-based applications, such as those provided and/or facilitated by machine learning techniques, but several limitations and open issues hamper the fulfillment of this plan. In this work we assess the possibility for homomorphic encryption to fully implement its program without the need to rely on other techniques, such as multiparty computation, which may be impossible in many actual use cases (for instance due to the high level of communication required). We proceed in two steps: i) on the basis of the well-known structured program theorem [28] 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 (which in turn require comparison and selection/jump operations) in full homomorphic encryption. We show how this issue has a serious impact and clashes with the fundamental requirements of homomorphic encryption. This could represent a drawback for its use as a complete solution in data analysis applications, in particular machine learning. It will thus possibly require a deep re-thinking of the homomorphic encryption program for privacy preservation. Read More
A review of homomorphic encryption and software tools for encrypted statistical machine learning
Recent advances in cryptography promise to enable secure statistical computation on encrypted data, whereby a limited set of operations can be carried out with out the need to first decrypt. We review these homomorphic encryption schemes in a manner accessible to statisticians and machine learners, focusing on pertinent limitations inherent in the current state of the art. These limitations restrict the kind of statistics and machine learning algorithms which can be implemented and we review those which have been successfully applied in the literature. Finally, we document a high performance R package implementing a recent homomorphic scheme in a general framework. Read More
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