scholarly journals Outsourced Mutual Private Set Intersection Protocol for Edge-Assisted IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jing Zhang ◽  
Rongxia Qin ◽  
Ruijie Mu ◽  
Xiaojun Wang ◽  
Yongli Tang
Keyword(s):  
Internet Of Things ◽  
Data Privacy ◽  
Encryption Algorithm ◽  
Computational Overhead ◽  
Private Data ◽  
Set Intersection ◽  
Security Problem ◽  
Private Set Intersection ◽  
Internet Of Things Technology ◽  
Threshold Encryption

The development of edge computing and Internet of Things technology has brought convenience to our lives, but the sensitive and private data collected are also more vulnerable to attack. Aiming at the data privacy security problem of edge-assisted Internet of Things, an outsourced mutual Private Set Intersection protocol is proposed. The protocol uses the ElGamal threshold encryption algorithm to rerandomize the encrypted elements to ensure all the set elements are calculated in the form of ciphertext. After that, the protocol maps the set elements to the corresponding hash bin under the execution of two hash functions and calculates the intersection in a bin-to-bin manner, reducing the number of comparisons of the set elements. In addition, the introduction of edge servers reduces the computational burden of participating users and achieves the fairness of the protocol. Finally, the IND-CPA security of the protocol is proved, and the performance of the protocol is compared with other relevant schemes. The evaluation results show that this protocol is superior to other related protocols in terms of lower computational overhead.

scholarly journals Optimization of Cross-Border e-Commerce Logistics Supervision System Based on Internet of Things Technology

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Pingping Sun ◽  
Lingang Gu
Keyword(s):  
Internet Of Things ◽  
Data Privacy ◽  
Evaluation Index System ◽  
Operating Efficiency ◽  
Service Platform ◽  
Actual Measurement ◽  
Supervision System ◽  
Logistics Service ◽  
Cross Border ◽  
Internet Of Things Technology

Based on the Internet of Things technology, this paper proposes building a cross-border e-commerce logistics supervision system and determines the evaluation index system from the overall framework design of the system, supply chain supervision process optimization, risk supervision optimization, and system order degree optimization. First of all, the framework adopts the national certification center to supervise the logistics service platform and logistics service platform to supervise the logistics participants of the secondary supervision system. Then, functions such as swarm intelligence contract, legal anonymous identity authentication, intelligent transaction matching, abnormal data analysis and detection, privacy protection, and traceability are realized under the framework of the supervision system. Then, the security analysis and transaction supervision component software are used to verify the security, control, and operating efficiency of the transaction supervision framework. Finally, in a real crowd sourcing logistics enterprise platform to run on the software component, the actual measurement, the measured results show that the proposed cross-border supervision system is safe and controllable, and electronic business logistics protects users and data privacy, prevents forgery and fraud, and realizes the user behavior and user data in addition to auditability and traceability.

scholarly journals Quantum private set intersection cardinality based on bloom filter

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bai Liu ◽  
Ou Ruan ◽  
Runhua Shi ◽  
Mingwu Zhang
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Communication Complexity ◽  
Single Photon ◽  
Bloom Filter ◽  
Distributed Database ◽  
Single Photons ◽  
Practical Applications ◽  
Set Intersection ◽  
Private Set Intersection

AbstractPrivate Set Intersection Cardinality that enable Multi-party to privately compute the cardinality of the set intersection without disclosing their own information. It is equivalent to a secure, distributed database query and has many practical applications in privacy preserving and data sharing. In this paper, we propose a novel quantum private set intersection cardinality based on Bloom filter, which can resist the quantum attack. It is a completely novel constructive protocol for computing the intersection cardinality by using Bloom filter. The protocol uses single photons, so it only need to do some simple single-photon operations and tests. Thus it is more likely to realize through the present technologies. The validity of the protocol is verified by comparing with other protocols. The protocol implements privacy protection without increasing the computational complexity and communication complexity, which are independent with data scale. Therefore, the protocol has a good prospects in dealing with big data, privacy-protection and information-sharing, such as the patient contact for COVID-19.

scholarly journals The Improved Hill Encryption Algorithm towards the Unmanned Surface Vessel Video Monitoring System Based on Internet of Things Technology

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Tingting Yang ◽  
Yangyang Li ◽  
Chengzhe Lai ◽  
Jie Dong ◽  
Minghua Xia
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
Video Compression ◽  
Computation Time ◽  
Video Data ◽  
Maritime Security ◽  
Video Monitoring ◽  
Encryption Algorithm ◽  
Security Technology ◽  
Internet Of Things Technology

Depending on the actual demand of maritime security, this paper analyzes the specific requirements of video encryption algorithm for maritime monitoring system. Based on the technology of Internet of things, the intelligent monitoring system of unmanned surface vessels (USV) is designed and realized, and the security technology and network technology of the Internet of things are adopted. The USV are utilized to monitor and collect information on the sea, which is critical to maritime security. Once the video data were captured by pirates and criminals during the transmission, the security of the sea will be affected awfully. The shortcomings of traditional algorithms are as follows: the encryption degree is not high, computing cost is expensive, and video data is intercepted and captured easily during the transmission process. In order to overcome the disadvantages, a novel encryption algorithm, i.e., the improved Hill encryption algorithm, is proposed to deal with the security problems of the unmanned video monitoring system in this paper. Specifically, the Hill algorithm of classical cryptography is transplanted into image encryption, using an invertible matrix as the key to realize the encryption of image matrix. The improved Hill encryption algorithm combines with the process of video compression and regulates the parameters of the encryption process according to the content of the video image and overcomes the disadvantages that exist in the traditional encryption algorithm and decreases the computation time of the inverse matrix so that the comprehensive performance of the algorithm is optimal with different image information. Experiments results validate the favorable performance of the proposed improved encryption algorithm.

Provably Secure Private Set Intersection With Constant Communication Complexity

2019 ◽  
Vol 9 (2) ◽  
pp. 39-64
Author(s):  
Sumit Kumar Debnath
Keyword(s):  
Communication Complexity ◽  
Bloom Filter ◽  
Security And Privacy ◽  
Data Sets ◽  
Indistinguishability Obfuscation ◽  
Security Parameter ◽  
Private Data ◽  
Set Intersection ◽  
Private Set Intersection ◽  
Probabilistic Data Structure

Electronic information is increasingly shared among unreliable entities. In this context, one interesting problem involves two parties that secretly want to determine an intersection of their respective private data sets while none of them wish to disclose the whole set to the other. One can adopt a Private Set Intersection (PSI) protocol to address this problem preserving the associated security and privacy issues. In this article, the authors present the first PSI protocol that incurs constant (p(k)) communication complexity with linear computation overhead and is fast even for the case of large input sets, where p(k) is a polynomial in security parameter k. Security of this scheme is proven in the standard model against semi-honest entities. The authors combine somewhere statistically binding (SSB) hash function with indistinguishability obfuscation (iO) and space-efficient probabilistic data structure Bloom filter to design the scheme.

scholarly journals Fusion Chain: A Decentralized Lightweight Blockchain for IoT Security and Privacy

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 391
Author(s):  
Dongjun Na ◽  
Sejin Park
Keyword(s):  
Data Privacy ◽  
Single Point ◽  
Consensus Algorithm ◽  
Security And Privacy ◽  
Public Key ◽  
Byzantine Fault Tolerance ◽  
Blockchain Technology ◽  
Private Data ◽  
Security Problem ◽  
Iot Devices

As the use of internet of things (IoT) devices increases, the importance of security has increased, because personal and private data such as biometrics, images, photos, and voices can be collected. However, there is a possibility of data leakage or manipulation by monopolizing the authority of the data, since such data are stored in a central server by the centralized structure of IoT devices. Furthermore, such a structure has a potential security problem, caused by an attack on the server due to single point vulnerability. Blockchain’s, through their decentralized structure, effectively solve the single point vulnerability, and their consensus algorithm allows network participants to verify data without any monopolizing. Therefore, blockchain technology becomes an effective solution for solving the security problem of the IoT’s centralized method. However, current blockchain technology is not suitable for IoT devices. Blockchain technology requires large storage space for the endless append-only block storing, and high CPU processing power for performing consensus algorithms, while its opened block access policy exposes private data to the public. In this paper, we propose a decentralized lightweight blockchain, named Fusion Chain, to support IoT devices. First, it solves the storage size issue of the blockchain by using the interplanetary file system (IPFS). Second, it does not require high computational power by using the practical Byzantine fault tolerance (PBFT) consensus algorithm. Third, data privacy is ensured by allowing only authorized users to access data through public key encryption using PKI. Fusion Chain was implemented from scratch written using Node.js and golang. The results show that the proposed Fusion Chain is suitable for IoT devices. According to our experiments, the size of the blockchain dramatically decreased, and only 6% of CPU on an ARM core, and 49 MB of memory, is used on average for the consensus process. It also effectively protects privacy data by using a public key infrastructure (PKI).

An Improvised Framework for Privacy Preservation in IoT

2021 ◽  
pp. 475-491
Author(s):  
Muzzammil Hussain ◽  
Neha Kaliya
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Execution Time ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Encryption Algorithm ◽  
The Public ◽  
Attribute Based Encryption ◽  
Key Length ◽  
Different Levels

Data privacy is now-a-days a special issue in era of Internet of Things because of the big data stored and transmitted by the public/private devices. Different types and levels of privacy can be provided at different layers of IoT architecture, also different mechanisms operate at different layers of IoT architecture. This article presents the work being done towards the design of a generic framework to integrate these privacy preserving mechanisms at different layers of IoT architecture and can ensure privacy preservation in a heterogeneous IoT environment. The data is classified into different levels of secrecy and appropriate rules and mechanisms are applied to ensure this privacy. The proposed framework is implemented and evaluated for its performance with security and execution time or primary parameters. Various scenarios are also evaluated, and a comparison is done with an existing mechanism ABE (Attribute Based Encryption). It has been found that the proposed work takes less time and is more secure due to short key length and randomness of the parameters used in encryption algorithm.

Privacy-preserving Collaborative Training for Medical Image Analysis Based on Multi-Blockchain

2020 ◽  
Vol 23 ◽  
Author(s):  
Wanlu Zhang ◽  
Qigang Wang ◽  
Mei Li
Keyword(s):  
Medical Image ◽  
Data Privacy ◽  
Medical Image Analysis ◽  
Auxiliary Information ◽  
Training Process ◽  
Private Data ◽  
Medical Institutions ◽  
Model Training ◽  
Collaborative Training ◽  
Similar Task

Background: As artificial intelligence and big data analysis develop rapidly, data privacy, especially patient medical data privacy, is getting more and more attention. Objective: To strengthen the protection of private data while ensuring the model training process, this article introduces a multi-Blockchain-based decentralized collaborative machine learning training method for medical image analysis. In this way, researchers from different medical institutions are able to collaborate to train models without exchanging sensitive patient data. Method: Partial parameter update method is applied to prevent indirect privacy leakage during model propagation. With the peer-to-peer communication in the multi-Blockchain system, a machine learning task can leverage auxiliary information from another similar task in another Blockchain. In addition, after the collaborative training process, personalized models of different medical institutions will be trained. Results: The experimental results show that our method achieves similar performance with the centralized model-training method by collecting data sets of all participants and prevents private data leakage at the same time. Transferring auxiliary information from similar task on another Blockchain has also been proven to effectively accelerate model convergence and improve model accuracy, especially in the scenario of absence of data. Personalization training process further improves model performance. Conclusion: Our approach can effectively help researchers from different organizations to achieve collaborative training without disclosing their private data.

scholarly journals Federated Learning in a Medical Context: A Systematic Literature Review

2021 ◽  
Vol 21 (2) ◽  
pp. 1-31
Author(s):  
Bjarne Pfitzner ◽  
Nico Steckhan ◽  
Bert Arnrich
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Privacy ◽  
Research Area ◽  
Learning Models ◽  
Related Data ◽  
Private Data ◽  
Large Databases ◽  
Machine Learning Models

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.

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