Integrity protection method for trusted data of IOT nodes based on transfer learning

2021 ◽  
pp. 1-11
Author(s):  
Lin Tang
Keyword(s):  
Transfer Learning ◽  
Data Storage ◽  
Learning Algorithm ◽  
Homomorphic Encryption ◽  
Mapping Function ◽  
Data Encryption ◽  
Complete Data ◽  
Protection Method ◽  
Integrity Protection ◽  
Trusted Data

In order to overcome the problems of high data storage occupancy and long encryption time in traditional integrity protection methods for trusted data of IOT node, this paper proposes an integrity protection method for trusted data of IOT node based on transfer learning. Through the transfer learning algorithm, the data characteristics of the IOT node is obtained, the feature mapping function in the common characteristics of the node data is set to complete the classification of the complete data and incomplete data in the IOT nodes. The data of the IOT nodes is input into the data processing database to verify its security, eliminate the node data with low security, and integrate the security data and the complete data. On this basis, homomorphic encryption algorithm is used to encrypt the trusted data of IOT nodes, and embedded processor is added to the IOT to realize data integrity protection. The experimental results show that: after using the proposed method to protect the integrity of trusted data of IOT nodes, the data storage occupancy rate is only about 3.5%, the shortest time-consuming of trusted data encryption of IOT nodes is about 3 s, and the work efficiency is high.

scholarly journals A Privacy-Preserving Reinforcement Learning Approach for Dynamic Treatment Regimes on Health Data

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Xiaoqiang Sun ◽  
Zhiwei Sun ◽  
Ting Wang ◽  
Jie Feng ◽  
Jiakai Wei ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Homomorphic Encryption ◽  
Treatment Decision ◽  
Data Encryption ◽  
Health Data ◽  
Secure Computation ◽  
Cloud Server ◽  
Dynamic Treatment ◽  
Reinforcement Learning Algorithm

Based on the clinical states of the patient, dynamic treatment regime technology can provide various therapeutic methods, which is helpful for medical treatment policymaking. Reinforcement learning is an important approach for developing this technology. In order to implement the reinforcement learning algorithm efficiently, the computation of health data is usually outsourced to the untrustworthy cloud server. However, it may leak, falsify, or delete private health data. Encryption is a common method for solving this problem. But the cloud server is difficult to calculate encrypted health data. In this paper, based on Cheon et al.’s approximate homomorphic encryption scheme, we first propose secure computation protocols for implementing comparison, maximum, exponentiation, and division. Next, we design a homomorphic reciprocal of square root protocol firstly, which only needs one approximate computation. Based on the proposed secure computation protocols, we design a secure asynchronous advantage actor-critic reinforcement learning algorithm for the first time. Then, it is used to implement a secure treatment decision-making algorithm. Simulation results show that our secure computation protocols and algorithms are feasible.

scholarly journals Lightweight Data Security Protection Method for AMI in Power Internet of Things

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Wenqian Jiang ◽  
Zhou Yang ◽  
Zhenglei Zhou ◽  
Jueyu Chen
Keyword(s):  
Internet Of Things ◽  
Data Aggregation ◽  
Data Security ◽  
Loss Rate ◽  
Homomorphic Encryption ◽  
Data Encryption ◽  
Smart Meters ◽  
Protection Method ◽  
New Business ◽  
Advanced Metering

Aiming at the security problems caused by the access of a large number of new advanced metering system (AMI) equipment and the rapid growth of new business data interaction volume and interaction frequency, a lightweight data security protection method for power Internet of things (IoT) is proposed. Firstly, based on the “cloud-edge-end” AMI system architecture, a multilevel anonymous authentication method is proposed to reduce the complexity of low-end equipment access without reauthentication when smart meters and other devices access the system. Then, when fully homomorphic encryption is used for data encryption transmission, the lightweight packet recombination protocol is introduced, the lightweight hash function is used to reduce the calculation cost, and the sliding address window mechanism is used to reduce the packet loss rate. Finally, improved secure multiparty computing (SMPC) is used to achieve frequency hopping data aggregation, using shared key to calculate local shared value for key update, reducing data interaction between massive devices and AMI cloud security server, and improving broadband utilization in data aggregation process. The experiment results indicate that the proposed method obtained better utilization in bandwidth and shorter average data collection completion time. Besides, the proposed method can ensure the information security in the interaction process.

scholarly journals A Homomorphic Encryption and Privacy Protection Method Based on Blockchain and Edge Computing

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaoyan Yan ◽  
Qilin Wu ◽  
Youming Sun
Keyword(s):  
Data Storage ◽  
Privacy Protection ◽  
Simulation Experiment ◽  
Homomorphic Encryption ◽  
Database Security ◽  
Edge Computing ◽  
Operating Efficiency ◽  
Cloud Data ◽  
Protection Method ◽  
Additive Homomorphism

With its decentralization, reliable database, security, and quasi anonymity, blockchain provides a new solution for data storage and sharing as well as privacy protection. This paper combines the advantages of blockchain and edge computing and constructs the key technology solutions of edge computing based on blockchain. On one hand, it achieves the security protection and integrity check of cloud data; and on the other hand, it also realizes more extensive secure multiparty computation. In order to assure the operating efficiency of blockchain and alleviate the computational burden of client, it also introduces the Paillier cryptosystem which supports additive homomorphism. The task execution side encrypts all data, while the edge node can process the ciphertext of the data received, acquire and return the ciphertext of the final result to the client. The simulation experiment proves that the proposed algorithm is effective and feasible.

scholarly journals A Decentralized Identity-Based Blockchain Solution for Privacy-Preserving Licensing of Individual-Controlled Data to Prevent Unauthorized Secondary Data Usage

Ledger ◽  
2021 ◽  
Vol 6 ◽  
Author(s):  
Meng Kang ◽  
Victoria Lemieux
Keyword(s):  
Data Storage ◽  
Identity Management ◽  
Homomorphic Encryption ◽  
Personal Data ◽  
Secondary Data ◽  
Usage Control ◽  
Secondary Use ◽  
Data Usage ◽  
Novel Approach ◽  
Trusted Data

This paper presents a design for a blockchain solution aimed at the prevention of unauthorized secondary use of data. This solution brings together advances from the fields of identity management, confidential computing, and advanced data usage control. In the area of identity management, the solution is aligned with emerging decentralized identity standards: decentralized identifiers (DIDs), DID communication and verifiable credentials (VCs). In respect to confidential computing, the Cheon-Kim-Kim-Song (CKKS) fully homomorphic encryption (FHE) scheme is incorporated with the system to protect the privacy of the individual’s data and prevent unauthorized secondary use when being shared with potential users. In the area of advanced data usage control, the solution leverages the PRIV-DRM solution architecture to derive a novel approach to licensing of data usage to prevent unauthorized secondary usage of data held by individuals. Specifically, our design covers necessary roles in the data-sharing ecosystem: the issuer of personal data, the individual holder of the personal data (i.e., the data subject), a trusted data storage manager, a trusted license distributor, and the data consumer. The proof-of-concept implementation utilizes the decentralized identity framework being developed by the Hyperledger Indy/Aries project. A genomic data licensing use case is evaluated, which shows the feasibility and scalability of the solution.

scholarly journals Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 885 ◽  
Author(s):  
Zhongzheng Fu ◽  
Xinrun He ◽  
Enkai Wang ◽  
Jun Huo ◽  
Jian Huang ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Sensor Node ◽  
Learning Algorithm ◽  
Joint Probability ◽  
Human Activity Recognition ◽  
Sensor Technology ◽  
Measurement Unit ◽  
Wearable Sensor

Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model’s generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%.

Event detection of different English data sources based on transfer learning

2021 ◽  
pp. 1-11
Author(s):  
Yanan Huang ◽  
Yuji Miao ◽  
Zhenjing Da
Keyword(s):  
Transfer Learning ◽  
Event Detection ◽  
Visual Analysis ◽  
Learning Algorithm ◽  
Data Sources ◽  
Data Set ◽  
Data Source ◽  
Single Data Source ◽  
The Difference ◽  
Single Data

The methods of multi-modal English event detection under a single data source and isomorphic event detection of different English data sources based on transfer learning still need to be improved. In order to improve the efficiency of English and data source time detection, based on the transfer learning algorithm, this paper proposes multi-modal event detection under a single data source and isomorphic event detection based on transfer learning for different data sources. Moreover, by stacking multiple classification models, this paper makes each feature merge with each other, and conducts confrontation training through the difference between the two classifiers to further make the distribution of different source data similar. In addition, in order to verify the algorithm proposed in this paper, a multi-source English event detection data set is collected through a data collection method. Finally, this paper uses the data set to verify the method proposed in this paper and compare it with the current most mainstream transfer learning methods. Through experimental analysis, convergence analysis, visual analysis and parameter evaluation, the effectiveness of the algorithm proposed in this paper is demonstrated.

scholarly journals E-ART: A New Encryption Algorithm Based on the Reflection of Binary Search Tree

Cryptography ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 4
Author(s):  
Bayan Alabdullah ◽  
Natalia Beloff ◽  
Martin White
Keyword(s):  
Data Storage ◽  
Statistical Tests ◽  
Data Encryption ◽  
Search Tree ◽  
Binary Search ◽  
Binary Search Tree ◽  
Security Level ◽  
Avalanche Effect ◽  
High Security Level ◽  
Tree Data Structure

Data security has become crucial to most enterprise and government applications due to the increasing amount of data generated, collected, and analyzed. Many algorithms have been developed to secure data storage and transmission. However, most existing solutions require multi-round functions to prevent differential and linear attacks. This results in longer execution times and greater memory consumption, which are not suitable for large datasets or delay-sensitive systems. To address these issues, this work proposes a novel algorithm that uses, on one hand, the reflection property of a balanced binary search tree data structure to minimize the overhead, and on the other hand, a dynamic offset to achieve a high security level. The performance and security of the proposed algorithm were compared to Advanced Encryption Standard and Data Encryption Standard symmetric encryption algorithms. The proposed algorithm achieved the lowest running time with comparable memory usage and satisfied the avalanche effect criterion with 50.1%. Furthermore, the randomness of the dynamic offset passed a series of National Institute of Standards and Technology (NIST) statistical tests.

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