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Search Results (33)
  • Open Access

    ARTICLE

    Secure Multi-Party Quantum Summation Based on Quantum Homomorphic Encryption

    Gang Xu1,2, Fan Yun1, Xiu-Bo Chen3,*, Shiyuan Xu1, Jingzhong Wang1, Tao Shang4, Yan Chang5, Mianxiong Dong6

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 531-541, 2022, DOI:10.32604/iasc.2022.028264 - 15 April 2022

    Abstract Secure multi-party computation has been playing a fundamental role in terms of classical cryptography. Quantum homomorphic encryption (QHE) could compute the encrypted data without decryption. At present, most protocols use a semi-honest third party (TP) to protect participants’ secrets. We use a quantum homomorphic encryption scheme instead of TP to protect the privacy of parties. Based on quantum homomorphic encryption, a secure multi-party quantum summation scheme is proposed in which N participants can delegate a server with strong quantum computing power to assist computation. By delegating the computation and key update processes to a server and… More >

  • Open Access

    ARTICLE

    Preserving Data Confidentiality in Association Rule Mining Using Data Share Allocator Algorithm

    D. Dhinakaran1,*, P. M. Joe Prathap2

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1877-1892, 2022, DOI:10.32604/iasc.2022.024509 - 24 March 2022

    Abstract These days, investigations of information are becoming essential for various associations all over the globe. By and large, different associations need to perform information examinations on their joined data sets. Privacy and security have become a relentless concern wherein business experts do not desire to contribute their classified transaction data. Therefore, there is a requirement to build a proficient methodology that can process the broad mixture of data and convert those data into meaningful knowledge to the user without forfeiting the security and privacy of individuals’ crude information. We devised two unique protocols for frequent… More >

  • Open Access

    ARTICLE

    Improved Homomorphic Encryption with Optimal Key Generation Technique for VANETs

    G. Tamilarasi1,*, K. Rajiv Gandhi2, V. Palanisamy1

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1273-1288, 2022, DOI:10.32604/iasc.2022.024687 - 08 February 2022

    Abstract In recent years, vehicle ad hoc networks (VANETs) have garnered considerable interest in the field of intelligent transportation systems (ITS) due to the added safety and preventive measures for drivers and passengers. Regardless of the benefits provided by VANET, it confronts various challenges, most notably in terms of user/message security and privacy. Due to the decentralised nature of VANET and its changeable topologies, it is difficult to detect rogue or malfunctioning nodes or users. Using an improved grasshopper optimization algorithm (IGOA-PHE) technique in VANETs, this research develops a new privacy-preserving partly homomorphic encryption with optimal More >

  • Open Access

    ARTICLE

    An Effective Blockchain Based Secure Searchable Encryption System

    Aitizaz Ali1, Mehedi Masud2, Ateeq ur Rehman3, Can Chen1, Mehmood4, Mohammad A. AlZain5, Jehad Ali6,*

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1183-1195, 2022, DOI:10.32604/iasc.2022.023930 - 08 February 2022

    Abstract Security of Patient health records (PHR) is the most important aspect of cryptography over the Internet due to its value and importance preferably in the medical Internet of Things (IoT). Search keywords access mechanism is one of the common approaches which is used to access PHR from database, but it is susceptible to various security vulnerabilities. Although Blockchain-enabled healthcare systems provide security, but it may lead to some loopholes in the existing schemes. However, these methods primarily focused on data storage, and blockchain is used as a database. In this paper, Blockchain as a distributed… More >

  • Open Access

    ARTICLE

    Verifiable Privacy-Preserving Neural Network on Encrypted Data

    Yichuan Liu1, Chungen Xu1,*, Lei Xu1, Lin Mei1, Xing Zhang2, Cong Zuo3

    Journal of Information Hiding and Privacy Protection, Vol.3, No.4, pp. 151-164, 2021, DOI:10.32604/jihpp.2021.026944 - 22 March 2022

    Abstract The widespread acceptance of machine learning, particularly of neural networks leads to great success in many areas, such as recommender systems, medical predictions, and recognition. It is becoming possible for any individual with a personal electronic device and Internet access to complete complex machine learning tasks using cloud servers. However, it must be taken into consideration that the data from clients may be exposed to cloud servers. Recent work to preserve data confidentiality has allowed for the outsourcing of services using homomorphic encryption schemes. But these architectures are based on honest but curious cloud servers,… More >

  • Open Access

    ARTICLE

    Road Distance Computation Using Homomorphic Encryption in Road Networks

    Haining Yu1, Lailai Yin1,*, Hongli Zhang1, Dongyang Zhan1,2, Jiaxing Qu3, Guangyao Zhang4

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3445-3458, 2021, DOI:10.32604/cmc.2021.019462 - 24 August 2021

    Abstract Road networks have been used in a wide range of applications to reduces the cost of transportation and improve the quality of related services. The shortest road distance computation has been considered as one of the most fundamental operations of road networks computation. To alleviate privacy concerns about location privacy leaks during road distance computation, it is desirable to have a secure and efficient road distance computation approach. In this paper, we propose two secure road distance computation approaches, which can compute road distance over encrypted data efficiently. An approximate road distance computation approach is More >

  • Open Access

    ARTICLE

    Blockchain-Based Decision Tree Classification in Distributed Networks

    Jianping Yu1,2,3, Zhuqing Qiao1, Wensheng Tang1,2,3,*, Danni Wang1, Xiaojun Cao4

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 713-728, 2021, DOI:10.32604/iasc.2021.017154 - 01 July 2021

    Abstract In a distributed system such as Internet of things, the data volume from each node may be limited. Such limited data volume may constrain the performance of the machine learning classification model. How to effectively improve the performance of the classification in a distributed system has been a challenging problem in the field of data mining. Sharing data in the distributed network can enlarge the training data volume and improve the machine learning classification model’s accuracy. In this work, we take data sharing and the quality of shared data into consideration and propose an efficient… More >

  • Open Access

    ARTICLE

    Cryptographic Based Secure Model on Dataset for Deep Learning Algorithms

    Muhammad Tayyab1,*, Mohsen Marjani1, N. Z. Jhanjhi1, Ibrahim Abaker Targio Hashim2, Abdulwahab Ali Almazroi3, Abdulaleem Ali Almazroi4

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1183-1200, 2021, DOI:10.32604/cmc.2021.017199 - 04 June 2021

    Abstract Deep learning (DL) algorithms have been widely used in various security applications to enhance the performances of decision-based models. Malicious data added by an attacker can cause several security and privacy problems in the operation of DL models. The two most common active attacks are poisoning and evasion attacks, which can cause various problems, including wrong prediction and misclassification of decision-based models. Therefore, to design an efficient DL model, it is crucial to mitigate these attacks. In this regard, this study proposes a secure neural network (NN) model that provides data security during model training… More >

  • Open Access

    ARTICLE

    Privacy-Preserving Genetic Algorithm Outsourcing in Cloud Computing

    Leqi Jiang1, 2, Zhangjie Fu1, 2, *

    Journal of Cyber Security, Vol.2, No.1, pp. 49-61, 2020, DOI:10.32604/jcs.2020.09308

    Abstract Genetic Algorithm (GA) has been widely used to solve various optimization problems. As the solving process of GA requires large storage and computing resources, it is well motivated to outsource the solving process of GA to the cloud server. However, the algorithm user would never want his data to be disclosed to cloud server. Thus, it is necessary for the user to encrypt the data before transmitting them to the server. But the user will encounter a new problem. The arithmetic operations we are familiar with cannot work directly in the ciphertext domain. In this More >

  • Open Access

    ARTICLE

    A Novel Privacy‐Preserving Multi‐Attribute Reverse Auction Scheme with Bidder Anonymity Using Multi‐Server Homomorphic Computation

    Wenbo Shi1, Jiaqi Wang2, Jinxiu Zhu3, YuPeng Wang4, Dongmin Choi5

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 171-181, 2019, DOI:10.31209/2018.100000053

    Abstract With the further development of Internet, the decision-making ability of the smart service is getting stronger and stronger, and the electronic auction is paid attention to as one of the ways of decision system. In this paper, a secure multi-attribute reverse auction protocol without the trusted third party is proposed. It uses the Paillier public key cryptosystem with homomorphism and combines with oblivious transfer and anonymization techniques. A single auction server easily collides with a bidder, in order to solve this problem, a single auction server is replaced with multiple auction servers. The proposed scheme More >

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