scholarly journals Privacy-Preserving Distributed Deep Learning via Homomorphic Re-Encryption

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 411 ◽  
Author(s):  
Fengyi Tang ◽  
Wei Wu ◽  
Jian Liu ◽  
Huimei Wang ◽  
Ming Xian
Keyword(s):  
Deep Learning ◽  
Data Privacy ◽  
Computational Cost ◽  
Privacy Preserving ◽  
Learning System ◽  
Stochastic Gradient Descent ◽  
Work Related ◽  
Learning Scheme ◽  
Security Properties ◽  
Secure Channels

The flourishing deep learning on distributed training datasets arouses worry about data privacy. The recent work related to privacy-preserving distributed deep learning is based on the assumption that the server and any learning participant do not collude. Once they collude, the server could decrypt and get data of all learning participants. Moreover, since the private keys of all learning participants are the same, a learning participant must connect to the server via a distinct TLS/SSL secure channel to avoid leaking data to other learning participants. To fix these problems, we propose a privacy-preserving distributed deep learning scheme with the following improvements: (1) no information is leaked to the server even if any learning participant colludes with the server; (2) learning participants do not need different secure channels to communicate with the server; and (3) the deep learning model accuracy is higher. We achieve them by introducing a key transform server and using homomorphic re-encryption in asynchronous stochastic gradient descent applied to deep learning. We show that our scheme adds tolerable communication cost to the deep learning system, but achieves more security properties. The computational cost of learning participants is similar. Overall, our scheme is a more secure and more accurate deep learning scheme for distributed learning participants.

scholarly journals PFDLIS: Privacy-Preserving and Fair Deep Learning Inference Service under Publicly Verifiable Covert Security Setting

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1488
Author(s):  
Fengyi Tang ◽  
Jialu Hao ◽  
Jian Liu ◽  
Huimei Wang ◽  
Ming Xian
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Data Privacy ◽  
Input Data ◽  
Computational Cost ◽  
Privacy Preserving ◽  
Model Parameters ◽  
Privacy Model ◽  
Security Properties

The recent popularity and widespread use of deep learning heralds an era of artificial intelligence. Thanks to the emergence of a deep learning inference service, non-professional clients can enjoy the improvements and profits brought by artificial intelligence as well. However, the input data of the client may be sensitive so that the client does not want to send its input data to the server. Similarly, the pre-trained model of the server is valuable and the server is unwilling to make the model parameters public. Therefore, we propose a privacy-preserving and fair scheme for a deep learning inference service based on secure three-party computation and making commitments under the publicly verifiable covert security setting. We demonstrate that our scheme has the following desirable security properties—input data privacy, model privacy and defamation freeness. Finally, we conduct extensive experiments to evaluate the performance of our scheme on MNIST dataset. The experimental results verify that our scheme can achieve the same prediction accuracy as the pre-trained model with acceptable extra computational cost.

scholarly journals Privacy-Preserving Deep Learning for the Detection of Protected Health Information in Real-World Data: Comparative Evaluation (Preprint)

2019 ◽  
Author(s):  
Sven Festag ◽  
Cord Spreckelsen
Keyword(s):  
Deep Learning ◽  
Health Information ◽  
Clinical Data ◽  
Real World ◽  
Privacy Preserving ◽  
Stochastic Gradient Descent ◽  
Data Sets ◽  
Learning Approaches ◽  
Protected Health Information ◽  
Private Data

BACKGROUND Collaborative privacy-preserving training methods allow for the integration of locally stored private data sets into machine learning approaches while ensuring confidentiality and nondisclosure. OBJECTIVE In this work we assess the performance of a state-of-the-art neural network approach for the detection of protected health information in texts trained in a collaborative privacy-preserving way. METHODS The training adopts distributed selective stochastic gradient descent (ie, it works by exchanging local learning results achieved on private data sets). Five networks were trained on separated real-world clinical data sets by using the privacy-protecting protocol. In total, the data sets contain 1304 real longitudinal patient records for 296 patients. RESULTS These networks reached a mean F1 value of 0.955. The gold standard centralized training that is based on the union of all sets and does not take data security into consideration reaches a final value of 0.962. CONCLUSIONS Using real-world clinical data, our study shows that detection of protected health information can be secured by collaborative privacy-preserving training. In general, the approach shows the feasibility of deep learning on distributed and confidential clinical data while ensuring data protection.

scholarly journals An Efficient Approach Based on Privacy-Preserving Deep Learning for Satellite Image Classification

2021 ◽  
Vol 13 (11) ◽  
pp. 2221
Author(s):  
Munirah Alkhelaiwi ◽  
Wadii Boulila ◽  
Jawad Ahmad ◽  
Anis Koubaa ◽  
Maha Driss
Keyword(s):  
Deep Learning ◽  
Data Privacy ◽  
Satellite Images ◽  
Homomorphic Encryption ◽  
Satellite Image ◽  
Privacy Preserving ◽  
Cloud Services ◽  
Encryption Scheme ◽  
Privacy Concerns ◽  
Efficient Approach

Satellite images have drawn increasing interest from a wide variety of users, including business and government, ever since their increased usage in important fields ranging from weather, forestry and agriculture to surface changes and biodiversity monitoring. Recent updates in the field have also introduced various deep learning (DL) architectures to satellite imagery as a means of extracting useful information. However, this new approach comes with its own issues, including the fact that many users utilize ready-made cloud services (both public and private) in order to take advantage of built-in DL algorithms and thus avoid the complexity of developing their own DL architectures. However, this presents new challenges to protecting data against unauthorized access, mining and usage of sensitive information extracted from that data. Therefore, new privacy concerns regarding sensitive data in satellite images have arisen. This research proposes an efficient approach that takes advantage of privacy-preserving deep learning (PPDL)-based techniques to address privacy concerns regarding data from satellite images when applying public DL models. In this paper, we proposed a partially homomorphic encryption scheme (a Paillier scheme), which enables processing of confidential information without exposure of the underlying data. Our method achieves robust results when applied to a custom convolutional neural network (CNN) as well as to existing transfer learning methods. The proposed encryption scheme also allows for training CNN models on encrypted data directly, which requires lower computational overhead. Our experiments have been performed on a real-world dataset covering several regions across Saudi Arabia. The results demonstrate that our CNN-based models were able to retain data utility while maintaining data privacy. Security parameters such as correlation coefficient (−0.004), entropy (7.95), energy (0.01), contrast (10.57), number of pixel change rate (4.86), unified average change intensity (33.66), and more are in favor of our proposed encryption scheme. To the best of our knowledge, this research is also one of the first studies that applies PPDL-based techniques to satellite image data in any capacity.

scholarly journals Privacy-Preserving Deep Learning for the Detection of Protected Health Information in Real-World Data: Comparative Evaluation

10.2196/14064 ◽  
2020 ◽  
Vol 4 (5) ◽  
pp. e14064 ◽  
Author(s):  
Sven Festag ◽  
Cord Spreckelsen
Keyword(s):  
Deep Learning ◽  
Health Information ◽  
Clinical Data ◽  
Real World ◽  
Privacy Preserving ◽  
Stochastic Gradient Descent ◽  
Data Sets ◽  
Learning Approaches ◽  
Protected Health Information ◽  
Private Data

Background Collaborative privacy-preserving training methods allow for the integration of locally stored private data sets into machine learning approaches while ensuring confidentiality and nondisclosure. Objective In this work we assess the performance of a state-of-the-art neural network approach for the detection of protected health information in texts trained in a collaborative privacy-preserving way. Methods The training adopts distributed selective stochastic gradient descent (ie, it works by exchanging local learning results achieved on private data sets). Five networks were trained on separated real-world clinical data sets by using the privacy-protecting protocol. In total, the data sets contain 1304 real longitudinal patient records for 296 patients. Results These networks reached a mean F1 value of 0.955. The gold standard centralized training that is based on the union of all sets and does not take data security into consideration reaches a final value of 0.962. Conclusions Using real-world clinical data, our study shows that detection of protected health information can be secured by collaborative privacy-preserving training. In general, the approach shows the feasibility of deep learning on distributed and confidential clinical data while ensuring data protection.

scholarly journals SoK: Privacy-Preserving Computation Techniques for Deep Learning

2021 ◽  
Vol 2021 (4) ◽  
pp. 139-162
Author(s):  
José Cabrero-Holgueras ◽  
Sergio Pastrana
Keyword(s):  
Deep Learning ◽  
Data Science ◽  
Homomorphic Encryption ◽  
Computational Cost ◽  
Privacy Preserving ◽  
Third Party ◽  
Sensitive Information ◽  
Computation Techniques ◽  
Practical Applications ◽  
Advantages And Disadvantages

Abstract Deep Learning (DL) is a powerful solution for complex problems in many disciplines such as finance, medical research, or social sciences. Due to the high computational cost of DL algorithms, data scientists often rely upon Machine Learning as a Service (MLaaS) to outsource the computation onto third-party servers. However, outsourcing the computation raises privacy concerns when dealing with sensitive information, e.g., health or financial records. Also, privacy regulations like the European GDPR limit the collection, distribution, and use of such sensitive data. Recent advances in privacy-preserving computation techniques (i.e., Homomorphic Encryption and Secure Multiparty Computation) have enabled DL training and inference over protected data. However, these techniques are still immature and difficult to deploy in practical scenarios. In this work, we review the evolution of the adaptation of privacy-preserving computation techniques onto DL, to understand the gap between research proposals and practical applications. We highlight the relative advantages and disadvantages, considering aspects such as efficiency shortcomings, reproducibility issues due to the lack of standard tools and programming interfaces, or lack of integration with DL frameworks commonly used by the data science community.

A novel privacy-preserving deep learning scheme without a cryptography component

2021 ◽  
Vol 94 ◽  
pp. 107325
Author(s):  
Chin-Yu Sun ◽  
Allen C.-H. Wu ◽  
TingTing Hwang
Keyword(s):  
Deep Learning ◽  
Privacy Preserving ◽  
Learning Scheme

Power Consumption Data Privacy-preserving Scheme Based on Improved Multi-key Fully Homomorphic Encryption

2021 ◽  
Vol 14 ◽  
Author(s):  
Yuancheng Li ◽  
Jiawen Yu
Keyword(s):  
Power Consumption ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Homomorphic Encryption ◽  
Computational Cost ◽  
Privacy Preserving ◽  
Secret Key ◽  
Multiple Data ◽  
Consumption Data ◽  
Secret Keys

Background: In the power Internet of Things (IoT), power consumption data faces the risk of privacy leakage. Traditional privacy-preserving schemes cannot ensure data privacy on the system, as the secret key pairs shall be shared between all the interior nodes once leaked. In addition, the general schemes only support summation algorithms, resulting in a lack of extensibility. Objective: To preserve the privacy of power consumption data, ensure the privacy of secret keys, and support multiple data processing methods, we propose an improved power consumption data privacy-preserving scheme. Method: Firstly, we have established a power IoT architecture based on edge computing. Then the data is encrypted with the multi-key fully homomorphic algorithm to realize the operation of ciphertext, without the restrictions of calculation type. Through the improved decryption algorithm, ciphertext that can be separately decrypted in cloud nodes is generated, which contributes to reducing communication costs and preventing data leakage. Results: The experimental results show that our scheme is more efficient than traditional schemes in privacy preservation. According to the variance calculation result, the proposed scheme has reached the application standard in terms of computational cost and is feasible for practical operation. Discussion: In the future, we plan to adopt a secure multi-party computation based scheme so that data can be managed locally with homomorphic encryption, so as to ensure data privacy.

scholarly journals An Effective Integrity Verification Scheme of Cloud Data Based on BLS Signature

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xiling Luo ◽  
Zequan Zhou ◽  
Lin Zhong ◽  
Jian Mao ◽  
Chaoyong Chen
Keyword(s):  
Data Privacy ◽  
Cloud Service ◽  
Privacy Preserving ◽  
Verification Scheme ◽  
Cloud Data ◽  
Integrity Verification ◽  
Main Challenge ◽  
Outsourced Data ◽  
Security Properties ◽  
Local Storage

Cloud storage services allow users to outsource their data remotely to save their local storage space and enable them to manage resources on demand. However, once users outsourced their data to the remote cloud platform, they lose the physical control of the data. How to ensure the integrity of outsourced data is the major concern of cloud users and also is the main challenge in the cloud service deployment. Limited by the communication and computation overheads, traditional hash-based integrity verification solutions in the stand-alone systems cannot be directly adopted in remote cloud storing environment. In this paper, we improve the previous privacy preserving model and propose an effective integrity verification scheme of cloud data based on BLS signature (EoCo), which ensures public audition and data privacy preserving. In addition, EoCo also supports batch auditing operations. We conducted theoretical analysis of our scheme, demonstrated its correctness and security properties, and evaluated the system performance as well.

A Collaborative Privacy-Preserving Deep Learning System in Distributed Mobile Environment

2016 ◽  
Author(s):  
Menghan Liu ◽  
Haotian Jiang ◽  
Jia Chen ◽  
Alaa Badokhon ◽  
Xuetao Wei ◽  
...  
Keyword(s):  
Deep Learning ◽  
Privacy Preserving ◽  
Learning System ◽  
Mobile Environment
Sign in / Sign up

Export Citation Format

Share Document