CryptoRNN - Privacy-Preserving Recurrent Neural Networks Using Homomorphic Encryption

2020 ◽  
pp. 245-253
Author(s):  
Maya Bakshi ◽  
Mark Last
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Homomorphic Encryption ◽  
Privacy Preserving

scholarly journals Privacy-preserving neural networks with Homomorphic encryption: Challenges and opportunities

2021 ◽  
Author(s):  
Bernardo Pulido-Gaytan ◽  
Andrei Tchernykh ◽  
Jorge M. Cortés-Mendoza ◽  
Mikhail Babenko ◽  
Gleb Radchenko ◽  
...  
Keyword(s):  
Neural Networks ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Third Party ◽  
Security Threats ◽  
Computing Power ◽  
Confidential Data ◽  
Challenges And Opportunities ◽  
Potential Applications ◽  
In Transit

AbstractClassical machine learning modeling demands considerable computing power for internal calculations and training with big data in a reasonable amount of time. In recent years, clouds provide services to facilitate this process, but it introduces new security threats of data breaches. Modern encryption techniques ensure security and are considered as the best option to protect stored data and data in transit from an unauthorized third-party. However, a decryption process is necessary when the data must be processed or analyzed, falling into the initial problem of data vulnerability. Fully Homomorphic Encryption (FHE) is considered the holy grail of cryptography. It allows a non-trustworthy third-party resource to process encrypted information without disclosing confidential data. In this paper, we analyze the fundamental concepts of FHE, practical implementations, state-of-the-art approaches, limitations, advantages, disadvantages, potential applications, and development tools focusing on neural networks. In recent years, FHE development demonstrates remarkable progress. However, current literature in the homomorphic neural networks is almost exclusively addressed by practitioners looking for suitable implementations. It still lacks comprehensive and more thorough reviews. We focus on the privacy-preserving homomorphic encryption cryptosystems targeted at neural networks identifying current solutions, open issues, challenges, opportunities, and potential research directions.

scholarly journals Privacy-Preserving Federated Neural Network Learning for Disease-Associated Cell Classification

2022 ◽  
Author(s):  
Sinem Sav ◽  
Jean-Philippe Bossuat ◽  
Juan R. Troncoso-Pastoriza ◽  
Manfred Claassen ◽  
Jean-Pierre Hubaux
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Network Architecture ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Model Parameters ◽  
Patient Privacy ◽  
Learning Models ◽  
Machine Learning Models

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data-silos. Sharing or centralizing the data of different healthcare institutions is, however, unfeasible or prohibitively difficult due to privacy regulations. In this work, we address this problem by using a novel privacy-preserving federated learning-based approach, PriCell, for complex machine learning models such as convolutional neural networks. PriCell relies on multiparty homomorphic encryption and enables the collaborative training of encrypted neural networks with multiple healthcare institutions. We preserve the confidentiality of each institutions' input data, of any intermediate values, and of the trained model parameters. We efficiently replicate the training of a published state-of-the-art convolutional neural network architecture in a decentralized and privacy-preserving manner. Our solution achieves an accuracy comparable to the one obtained with the centralized solution, with an improvement of at least one-order-of-magnitude in execution time with respect to prior secure solutions. Our work guarantees patient privacy and ensures data utility for efficient multi-center studies involving complex healthcare data.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

LatticeRnn: Recurrent Neural Networks Over Lattices

2016 ◽  
Author(s):  
Faisal Ladhak ◽  
Ankur Gandhe ◽  
Markus Dreyer ◽  
Lambert Mathias ◽  
Ariya Rastrow ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks
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