Privacy-preserving Decentralized Learning Framework for Healthcare System

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-24
Author(s):  
Harsh Kasyap ◽  
Somanath Tripathy
Keyword(s):  
Privacy Preserving ◽  
Mitigation Strategies ◽  
Learning System ◽  
Data Repository ◽  
Security Issues ◽  
Learning Framework ◽  
Decentralized Learning ◽  
Potential Contributor ◽  
Model Training ◽  
The Cost

Clinical trials and drug discovery would not be effective without the collaboration of institutions. Earlier, it has been at the cost of individual’s privacy. Several pacts and compliances have been enforced to avoid data breaches. The existing schemes collect the participant’s data to a central repository for learning predictions as the collaboration is indispensable for research advances. The current COVID pandemic has put a question mark on our existing setup where the existing data repository has proved to be obsolete. There is a need for contemporary data collection, processing, and learning. The smartphones and devices held by the last person of the society have also made them a potential contributor. It demands to design a distributed and decentralized Collaborative Learning system that would make the knowledge inference from every data point. Federated Learning [21], proposed by Google, brings the concept of in-place model training by keeping the data intact to the device. Though it is privacy-preserving in nature, however, it is susceptible to inference, poisoning, and Sybil attacks. Blockchain is a decentralized programming paradigm that provides a broader control of the system, making it attack resistant. It poses challenges of high computing power, storage, and latency. These emerging technologies can contribute to the desired learning system and motivate them to address their security and efficiency issues. This article systematizes the security issues in Federated Learning, its corresponding mitigation strategies, and Blockchain’s challenges. Further, a Blockchain-based Federated Learning architecture with two layers of participation is presented, which improves the global model accuracy and guarantees participant’s privacy. It leverages the channel mechanism of Blockchain for parallel model training and distribution. It facilitates establishing decentralized trust between the participants and the gateways using the Blockchain, which helps to have only honest participants.

scholarly journals Privacy-Preserving Federated Learning Framework with General Aggregation and Multiparty Entity Matching

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhou Zhou ◽  
Youliang Tian ◽  
Changgen Peng
Keyword(s):  
Data Privacy ◽  
Privacy Preserving ◽  
Entity Matching ◽  
Learning Framework ◽  
Learning Tasks ◽  
Learning Scenarios ◽  
Model Training ◽  
Logistic Regression Algorithm ◽  
Shamir Secret Sharing ◽  
Training And Support

The requirement for data sharing and privacy has brought increasing attention to federated learning. However, the existing aggregation models are too specialized and deal less with users’ withdrawal issue. Moreover, protocols for multiparty entity matching are rarely covered. Thus, there is no systematic framework to perform federated learning tasks. In this paper, we systematically propose a privacy-preserving federated learning framework (PFLF) where we first construct a general secure aggregation model in federated learning scenarios by combining the Shamir secret sharing with homomorphic cryptography to ensure that the aggregated value can be decrypted correctly only when the number of participants is greater than t . Furthermore, we propose a multiparty entity matching protocol by employing secure multiparty computing to solve the entity alignment problems and a logistic regression algorithm to achieve privacy-preserving model training and support the withdrawal of users in vertical federated learning (VFL) scenarios. Finally, the security analyses prove that PFLF preserves the data privacy in the honest-but-curious model, and the experimental evaluations show PFLF attains consistent accuracy with the original model and demonstrates the practical feasibility.

scholarly journals Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

2021 ◽  
Vol 13 (4) ◽  
pp. 94
Author(s):  
Haokun Fang ◽  
Quan Qian
Keyword(s):  
Machine Learning ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Great Success ◽  
Learning Framework ◽  
Computational Overhead ◽  
Important Concern ◽  
Speed Up ◽  
Key Length ◽  
Core Idea

Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated learning. The core idea is all learning parties just transmitting the encrypted gradients by homomorphic encryption. From experiments, the model trained by PFMLP has almost the same accuracy, and the deviation is less than 1%. Considering the computational overhead of homomorphic encryption, we use an improved Paillier algorithm which can speed up the training by 25–28%. Moreover, comparisons on encryption key length, the learning network structure, number of learning clients, etc. are also discussed in detail in the paper.

Sparse Incremental Delta-Bar-Delta for System Identification

2014 ◽  
Vol 665 ◽  
pp. 643-646
Author(s):  
Ying Liu ◽  
Yan Ye ◽  
Chun Guang Li
Keyword(s):  
System Identification ◽  
Cost Function ◽  
Learning Algorithm ◽  
Learning System ◽  
The Other ◽  
Sparse System ◽  
Speed Up ◽  
Sparse System Identification ◽  
The Cost ◽  
Zero Attractor

Metalearning algorithm learns the base learning algorithm, targeted for improving the performance of the learning system. The incremental delta-bar-delta (IDBD) algorithm is such a metalearning algorithm. On the other hand, sparse algorithms are gaining popularity due to their good performance and wide applications. In this paper, we propose a sparse IDBD algorithm by taking the sparsity of the systems into account. Thenorm penalty is contained in the cost function of the standard IDBD, which is equivalent to adding a zero attractor in the iterations, thus can speed up convergence if the system of interest is indeed sparse. Simulations demonstrate that the proposed algorithm is superior to the competing algorithms in sparse system identification.

LIGHTWEIGHT CRYPTOGRAPHIC ALGORITHM IMPLEMENTATION IN A MICROCONTROLLER SYSTEM

2021 ◽  
Vol 17 (1) ◽  
pp. 260-264
Author(s):  
Alexandru VULPE ◽  
Raluca ANDREI ◽  
Alexandru BRUMARU ◽  
Octavian FRATU
Keyword(s):  
The Internet ◽  
Continuous Increase ◽  
Security Issues ◽  
Cryptographic Algorithm ◽  
Physical Limitations ◽  
Encryption Algorithms ◽  
Iot Devices ◽  
The Cost ◽  
Algorithm Implementation ◽  
Lightweight Encryption

Abstract: With the development of mobile devices and the advent of smartphones, the Internet has become part of everyday life. Any category of information about weather, flight schedule, etc. it is just a click away from the keyboard. This availability of data has led to a continuous increase in connectivity between devices, from any corner of the world. Combining device connectivity with systems automation allows the collection of information, its analysis and implicitly decision-making on the basis of information. Their introduction and continued expansion of devices that communicate in networks (including the Internet) have made security issues very important devices as well as for users. One of the main methodologies that ensures data confidentiality is encryption, which protects data from unauthorized access, but at the cost of using extensive mathematical models. Due to the nature of IoT devices, the resources allocated to a device can be constrained by certain factors, some of which are related to costs and others to the physical limitations of the device. Ensuring the confidentiality of data requires the use of encryption algorithms for these interconnected devices, which provide protection while maintaining the operation of that device. The need for these types of algorithms has created conditions for the growth and development of the concept of lightweight encryption, which aim to find encryption systems that can be implemented on these categories of devices, with limited hardware and software requirements. The paper proposes a lightweight cryptographic algorithm implemented on a microcontroller system, comparing its performances with those of the already existing system (based on x86).

scholarly journals Privacy-preserving energy storage sharing with blockchain and secure multi-party computation

2021 ◽  
Vol 1 (1) ◽  
pp. 32-50
Author(s):  
Nan Wang ◽  
Sid Chi-Kin Chau ◽  
Yue Zhou
Keyword(s):  
Energy Storage ◽  
Energy Demand ◽  
Empirical Evaluation ◽  
Privacy Preserving ◽  
Third Party ◽  
User Privacy ◽  
External Energy ◽  
Novel Approach ◽  
Significant Cost Reduction ◽  
The Cost

Energy storage provides an effective way of shifting temporal energy demands and supplies, which enables significant cost reduction under time-of-use energy pricing plans. Despite its promising benefits, the cost of present energy storage remains expensive, presenting a major obstacle to practical deployment. A more viable solution to improve the cost-effectiveness is by sharing energy storage, such as community sharing, cloud energy storage and peer-to-peer sharing. However, revealing private energy demand data to an external energy storage operator may compromise user privacy, and is susceptible to data misuses and breaches. In this paper, we explore a novel approach to support energy storage sharing with privacy protection, based on privacy-preserving blockchain and secure multi-party computation. We present an integrated solution to enable privacy-preserving energy storage sharing, such that energy storage service scheduling and cost-sharing can be attained without the knowledge of individual users' demands. It also supports auditing and verification by the grid operator via blockchain. Furthermore, our privacy-preserving solution can safeguard against a majority of dishonest users, who may collude in cheating, without requiring a trusted third-party. We implemented our solution as a smart contract on real-world Ethereum blockchain platform, and provided empirical evaluation in this paper 1 .

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