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2022-01-10
Radhakrishnan, Sangeetha, Akila, A..  2021.  Securing Distributed Database Using Elongated RSA Algorithm. 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). 1:1931–1936.
Securing data, management of the authorised access of the user and maintaining the privacy of the data are some of the problems relating with the stored data in the database. The security of the data stored is considered as the major concern which is to be managed in a very serious manner as the users are sensitive about their shared data. The user's data can be protected by the process of cryptography which is considered as the conventional method. Advanced Encryption Standard (AES), Data Encryption Standard(DES), Two Fish, Rivest Shamir Adleman Algorithm (RSA), Attribute Based Encryption (ABE), Blowfish algorithms are considered as some of the cryptographic algorithms. These algorithms are classified into symmetric and asymmetric algorithms. Same key is used for the encryption and decoding technique in symmetric key cryptographic algorithm whereas two keys are used for the asymmetric ones. In this paper, the implementation of one of the asymmetric algorithm RSA with the educational dataset is done. To secure the distributed database, the extended version of the RSA algorithm is implemented as the proposed work.
2021-11-30
Wagh, Gaurav S., Mishra, Sumita.  2020.  A Cyber-Resilient Privacy Framework for the Smart Grid with Dynamic Billing Capabilities. 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :1–6.
The desired features for the smart grid include dynamic billing capabilities along with consumer privacy protection. Existing aggregation-based privacy frameworks have limitations such as centralized designs prone to single points of failure and/or a high computational overload on the smart meters due to in-network aggregation or complex algorithmic operations. Additionally, these existing schemes do not consider how dynamic billing can be implemented while consumer privacy is preserved. In this paper, a cyber-resilient framework that enables dynamic billing while focusing on consumer privacy preservation is proposed. The distributed design provides a framework for spatio-temporal aggregation and keeps the process lightweight for the smart meters. The comparative analysis of our proposed work with existing work shows a significant improvement in terms of the spatial aggregation overhead, overhead on smart meters and scalability. The paper also discusses the resilience of our framework against privacy attacks.
2021-11-29
Yatskiv, Vasyl, Kulyna, Serhii, Yatskiv, Nataliya, Kulyna, Halyna.  2020.  Protected Distributed Data Storage Based on Residue Number System and Cloud Services. 2020 10th International Conference on Advanced Computer Information Technologies (ACIT). :796–799.
The reliable distributed data storage system based on the Redundant Residue Number System (RRNS) is developed. The structure of the system, data splitting and recovery algorithms based on RRNS are developed. A study of the total time and time spent on converting ASCII-encoded data into a RRNS for files of various sizes is conducted. The research of data recovery time is conducted for the inverse transformation from RRNS to ASCII codes.
2021-10-12
Sun, Yuxin, Zhang, Yingzhou, Zhu, Linlin.  2020.  An Anti-Collusion Fingerprinting based on CFF Code and RS Code. 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). :56–63.
Data security is becoming more and more important in data exchange. Once the data is leaked, it will pose a great threat to the privacy and property security of users. Copyright authentication and data provenance have become an important requirement of the information security defense mechanism. In order to solve the collusion leakage of the data distributed by organization and the low efficiency of tracking the leak provenance after the data is destroyed, this paper proposes a concatenated-group digital fingerprint coding based on CFF code and Reed-solomon (RS) that can resist collusion attacks and corresponding detection algorithm. The experiments based on an asymmetric anti-collusion fingerprint protocol show that the proposed method has better performance to resist collusion attacks than similar non-grouped fingerprint coding and effectively reduces the percentage of misjudgment, which verifies the availability of the algorithm and enriches the means of organization data security audit.
2021-10-04
Sayed, Ammar Ibrahim El, Aziz, Mahmoud Abdel, Azeem, Mohamed Hassan Abdel.  2020.  Blockchain Decentralized IoT Trust Management. 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT). :1–6.
IoT adds more flexibility in many areas of applications to makes it easy to monitor and manage data instantaneously. However, IoT has many challenges regarding its security and storage issues. Moreover, the third-party trusting agents of IoT devices do not support sufficient security level between the network peers. This paper proposes improving the trust, processing power, and storage capability of IoT in distributed system topology by adopting the blockchain approach. An application, IoT Trust Management (ITM), is proposed to manage the trust of the shared content through the blockchain network, e.g., supply chain. The essential key in ITM is the trust management of IoT devices data are done using peer to peer (P2P), i.e., no third-party. ITM is running on individual python nodes and interact with frontend applications creating decentralized applications (DApps). The IoT data shared and stored in a ledger, which has the IoT device published details and data. ITM provides a higher security level to the IoT data shared on the network, such as unparalleled security, speed, transparency, cost reduction, check data, and Adaptability.
Masood, Raziqa, Pandey, Nitin, Rana, Q. P..  2020.  DHT-PDP: A Distributed Hash Table based Provable Data Possession Mechanism in Cloud Storage. 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). :275–279.
The popularity of cloud storage among data users is due to easy maintenance, and no initial infrastructure setup cost as compared to local storage. However, although the data users outsource their data to cloud storage (a third party) still, they concern about their physical data. To check whether the data stored in the cloud storage has been modified or not, public auditing of the data is required before its utilization. To audit over vast outsourced data, the availability of the auditor is an essential requirement as nowadays, data owners are using mobile devices. But unfortunately, a single auditor leads to a single point of failure and inefficient to preserve the security and correctness of outsourced data. So, we introduce a distributed public auditing scheme which is based on peer-to-peer (P2P) architecture. In this work, the auditors are organized using a distributed hash table (DHT) mechanism and audit the outsourced data with the help of a published hashed key of the data. The computation and communication overhead of our proposed scheme is compared with the existing schemes, and it found to be an effective solution for public auditing on outsourced data with no single point of failure.
Badran, Sultan, Arman, Nabil, Farajallah, Mousa.  2020.  Towards a Hybrid Data Partitioning Technique for Secure Data Outsourcing. 2020 21st International Arab Conference on Information Technology (ACIT). :1–9.
In light of the progress achieved by the technology sector in the areas of internet speed and cloud services development, and in addition to other advantages provided by the cloud such as reliability and easy access from anywhere and anytime, most data owners find an opportunity to take advantage of the cloud to store data. However, data owners find a challenge that was and is still facing them in the field of outsourcing, which is protecting sensitive data from leakage. Researchers found that partitioning data into partitions, based on data sensitivity, can be used to protect data from leakage and to increase performance by storing the partition, which contains sensitive data in an encrypted form. In this paper, we review the methods used in designing partitions and dividing data approaches. A hybrid data partitioning approach is proposed to improve these techniques. We consider the frequency attack types used to guess the sensitive data and the most important properties that must be available in order for the encryption to be strong against frequency attacks.
2021-09-16
Du, Xin, Tang, Songtao, Lu, Zhihui, Wet, Jie, Gai, Keke, Hung, Patrick C.K..  2020.  A Novel Data Placement Strategy for Data-Sharing Scientific Workflows in Heterogeneous Edge-Cloud Computing Environments. 2020 IEEE International Conference on Web Services (ICWS). :498–507.
The deployment of datasets in the heterogeneous edge-cloud computing paradigm has received increasing attention in state-of-the-art research. However, due to their large sizes and the existence of private scientific datasets, finding an optimal data placement strategy that can minimize data transmission as well as improve performance, remains a persistent problem. In this study, the advantages of both edge and cloud computing are combined to construct a data placement model that works for multiple scientific workflows. Apparently, the most difficult research challenge is to provide a data placement strategy to consider shared datasets, both within individual and among multiple workflows, across various geographically distributed environments. According to the constructed model, not only the storage capacity of edge micro-datacenters, but also the data transfer between multiple clouds across regions must be considered. To address this issue, we considered the characteristics of this model and identified the factors that are causing the transmission delay. The authors propose using a discrete particle swarm optimization algorithm with differential evolution (DE-DPSO) to distribute dataset during workflow execution. Based on this, a new data placement strategy named DE-DPSO-DPS is proposed. DE-DPSO-DPS is evaluated using several experiments designed in simulated heterogeneous edge-cloud computing environments. The results demonstrate that our data placement strategy can effectively reduce the data transmission time and achieve superior performance as compared to traditional strategies for data-sharing scientific workflows.
Rieger, Craig, Kolias, Constantinos, Ulrich, Jacob, McJunkin, Timothy R..  2020.  A Cyber Resilient Design for Control Systems. 2020 Resilience Week (RWS). :18–25.
The following topics are dealt with: security of data; distributed power generation; power engineering computing; power grids; power system security; computer network security; voltage control; risk management; power system measurement; critical infrastructures.
2021-09-07
Shi, Jiayu, Wu, Bin.  2020.  Detection of DDoS Based on Gray Level Co-Occurrence Matrix Theory and Deep Learning. 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). :1615–1618.
There have been researches on Distributed Denial of Service (DDoS) attack detection based on deep learning, but most of them use the feature data processed by data mining for feature learning and classification. Based on the original data flow, this paper combines the method of Gray Level Co-occurrence Matrix (GLCM), which not only retains the original data but also can further extract the potential relationship between the original data. The original data matrix and the reconstructed matrix were taken as the input of the model, and the Convolutional Neural Network(CNN) was used for feature learning. Finally, the classifier model was trained for detection. The experimental part is divided into two parts: comparing the detection effect of different data processing methods and different deep learning algorithms; the effectiveness and objectivity of the proposed method are verified by comparing the detection effect of the deep learning algorithm with that of the statistical analysis feature algorithm.
2021-08-31
Bartol, Janez, Souvent, Andrej, Suljanović, Nermin, Zajc, Matej.  2020.  Secure data exchange between IoT endpoints for energy balancing using distributed ledger. 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). :56—60.
This paper investigates a secure data exchange between many small distributed consumers/prosumers and the aggregator in the process of energy balancing. It addresses the challenges of ensuring data exchange in a simple, scalable, and affordable way. The communication platform for data exchange is using Ethereum Blockchain technology. It provides a distributed ledger database across a distributed network, supports simple connectivity for new stakeholders, and enables many small entities to contribute with their flexible energy to the system balancing. The architecture of a simulation/emulation environment provides a direct connection of a relational database to the Ethereum network, thus enabling dynamic data management. In addition, it extends security of the environment with security mechanisms of relational databases. Proof-of-concept setup with the simulation of system balancing processes, confirms the suitability of the solution for secure data exchange in the market, operation, and measurement area. For the most intensive and space-consuming measurement data exchange, we have investigated data aggregation to ensure performance optimisation of required computation and space usage.
Vonitsanos, Gerasimos, Dritsas, Elias, Kanavos, Andreas, Mylonas, Phivos, Sioutas, Spyros.  2020.  Security and Privacy Solutions associated with NoSQL Data Stores. 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA). :1—5.
Technologies such as cloud computing and big data management, have lately made significant progress creating an urgent need for specific databases that can safely store extensive data along with high availability. Specifically, a growing number of companies have adopted various types of non-relational databases, commonly referred to as NoSQL databases. These databases provide a robust mechanism for the storage and retrieval of large amounts of data without using a predefined schema. NoSQL platforms are superior to RDBMS, especially in cases when we are dealing with big data and parallel processing, and in particular, when there is no need to use relational modeling. Sensitive data is stored daily in NoSQL Databases, making the privacy problem more serious while raising essential security issues. In our paper, security and privacy issues when dealing with NoSQL databases are introduced and in following, security mechanisms and privacy solutions are thoroughly examined.
2021-08-17
Chen, Congwei, Elsayed, Marwa A., Zulkernine, Mohammad.  2020.  HBD-Authority: Streaming Access Control Model for Hadoop. 2020 IEEE 6th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application (DependSys). :16–25.
Big data analytics, in essence, is becoming the revolution of business intelligence around the world. This momentum has given rise to the hype around analytic technologies, including Apache Hadoop. Hadoop was not originally developed with security in mind. Despite the evolving efforts to integrate security in Hadoop through developing new tools (e.g., Apache Sentry and Ranger) and employing traditional mechanisms (e.g., Kerberos and LDAP), they mainly focus on providing encryption and authentication features, albeit with limited authorization support. Existing solutions in the literature extended these evolving efforts. However, they suffer from limitations, hindering them from providing robust authorization that effectively meets the unique requirements of big data environments. Towards covering this gap, this paper proposes a hybrid authority (HBD-Authority) as a formal attribute-based access control model with context support. This model is established on a novel hybrid approach of authorization transparency that pertains to three fundamental properties of accuracy: correctness, security, and completeness. The model leverages streaming data analytics to foster distributed parallel processing capabilities that achieve multifold benefits: a) efficiently managing the security policies and promptly updating the privileges assigned to a high number of users interacting with the analytic services; b) swiftly deciding and enforcing authorization of requests over data characterized by the 5Vs; and c) providing dynamic protection for data which is frequently updated. The implementation details and experimental evaluation of the proposed model are presented, demonstrating its performance efficiency.
2021-07-27
Yang, Chien-Sheng, Avestimehr, A. Salman.  2020.  Coded Computing for Boolean Functions. 2020 International Symposium on Information Theory and Its Applications (ISITA). :141–145.
The growing size of modern datasets necessitates splitting a large scale computation into smaller computations and operate in a distributed manner for improving overall performance. However, adversarial servers in a distributed computing system deliberately send erroneous data in order to affect the computation for their benefit. Computing Boolean functions is the key component of many applications of interest, e.g., classification problem, verification functions in the blockchain and the design of cryptographic algorithm. In this paper, we consider the problem of computing a Boolean function in which the computation is carried out distributively across several workers with particular focus on security against Byzantine workers. We note that any Boolean function can be modeled as a multivariate polynomial which can have high degree in general. Hence, the recently proposed Lagrange Coded Computing (LCC) can be used to simultaneously provide resiliency, security, and privacy. However, the security threshold (i.e., the maximum number of adversarial workers that can be tolerated) provided by LCC can be extremely low if the degree of the polynomial is high. Our goal is to design an efficient coding scheme which achieves the optimal security threshold. We propose two novel schemes called coded Algebraic normal form (ANF) and coded Disjunctive normal form (DNF). Instead of modeling the Boolean function as a general polynomial, the key idea of the proposed schemes is to model it as the concatenation of some linear functions and threshold functions. The proposed coded ANF and coded DNF outperform LCC by providing the security threshold which is independent of the polynomial's degree.
Basu, Prithwish, Salonidis, Theodoros, Kraczek, Brent, Saghaian, Sayed M., Sydney, Ali, Ko, Bongjun, La Porta, Tom, Chan, Kevin.  2020.  Decentralized placement of data and analytics in wireless networks for energy-efficient execution. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :486—495.
We address energy-efficient placement of data and analytics components of composite analytics services on a wireless network to minimize execution-time energy consumption (computation and communication) subject to compute, storage and network resource constraints. We introduce an expressive analytics service hypergraph model for representing k-ary composability relationships (k ≥ 2) between various analytics and data components and leverage binary quadratic programming (BQP) to minimize the total energy consumption of a given placement of the analytics hypergraph nodes on the network subject to resource availability constraints. Then, after defining a potential energy functional Φ(·) to model the affinities of analytics components and network resources using analogs of attractive and repulsive forces in physics, we propose a decentralized Metropolis Monte Carlo (MMC) sampling method which seeks to minimize Φ by moving analytics and data on the network. Although Φ is non-convex, using a potential game formulation, we identify conditions under which the algorithm provably converges to a local minimum energy equilibrium placement configuration. Trace-based simulations of the placement of a deep-neural-network analytics service on a realistic wireless network show that for smaller problem instances our MMC algorithm yields placements with total energy within a small factor of BQP and more balanced workload distributions; for larger problems, it yields low-energy configurations while the BQP approach fails.
2021-07-07
Yang, Yuanyuan, Li, Hui, Cheng, Xiangdong, Yang, Xin, Huo, Yaoguang.  2020.  A High Security Signature Algorithm Based on Kerberos for REST-style Cloud Storage Service. 2020 11th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON). :0176–0182.
The Representational State Transfer (REST) is a distributed application architecture style which adopted on providing various network services. The identity authentication protocol Kerberos has been used to guarantee the security identity authentication of many service platforms. However, the deployment of Kerberos protocol is limited by the defects such as password guessing attacks, data tampering, and replay attacks. In this paper, an optimized Kerberos protocol is proposed and applied in a REST-style Cloud Storage Architecture. Firstly, we propose a Lately Used Newly (LUN) key replacement method to resist the password guessing attacks in Kerberos protocol. Secondly, we propose a formatted signature algorithm and a combination of signature string and time stamp method to cope with the problems of tampering and replay attacks which in deploying Kerberos. Finally, we build a security protection module using the optimized Kerberos protocol to guarantee a secure identity authentication and the reliable data communication between the client and the server. Analyses show that the module significantly improves the security of Kerberos protocol in REST-style cloud storage services.
2021-06-30
Xu, Hui, Zhang, Wei, Gao, Man, Chen, Hongwei.  2020.  Clustering Analysis for Big Data in Network Security Domain Using a Spark-Based Method. 2020 IEEE 5th International Symposium on Smart and Wireless Systems within the Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS). :1—4.
Considering the problem of network security under the background of big data, the clustering analysis algorithms can be utilized to improve the correctness of network intrusion detection models for security management. As a kind of iterative clustering analysis algorithm, K-means algorithm is not only simple but also efficient, so it is widely used. However, the traditional K-means algorithm cannot well solve the network security problem when facing big data due to its high complexity and limited processing ability. In this case, this paper proposes to optimize the traditional K-means algorithm based on the Spark platform and deploy the optimized clustering analysis algorithm in the distributed architecture, so as to improve the efficiency of clustering algorithm for network intrusion detection in big data environment. The experimental result shows that, compared with the traditional K-means algorithm, the efficiency of the optimized K-means algorithm using a Spark-based method is significantly improved in the running time.
2021-04-27
Sidhu, H. J. Singh, Khanna, M. S..  2020.  Cloud's Transformative Involvement in Managing BIG-DATA ANALYTICS For Securing Data in Transit, Storage And Use: A Study. 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC). :297—302.

with the advent of Cloud Computing a new era of computing has come into existence. No doubt, there are numerous advantages associated with the Cloud Computing but, there is other side of the picture too. The challenges associated with it need a more promising reply as far as the security of data that is stored, in process and in transit is concerned. This paper put forth a cloud computing model that tries to answer the data security queries; we are talking about, in terms of the four cryptographic techniques namely Homomorphic Encryption (HE), Verifiable Computation (VC), Secure Multi-Party Computation (SMPC), Functional Encryption (FE). This paper takes into account the various cryptographic techniques to undertake cloud computing security issues. It also surveys these important (existing) cryptographic tools/techniques through a proposed Cloud computation model that can be used for Big Data applications. Further, these cryptographic tools are also taken into account in terms of CIA triad. Then, these tools/techniques are analyzed by comparing them on the basis of certain parameters of concern.

Zhang, Z., Wang, F., Zhong, C., Ma, H..  2020.  Grid Terminal Data Security Management Mechanism Based On Master-Slave Blockchain. 2020 5th International Conference on Computer and Communication Systems (ICCCS). :67—70.

In order to design an end-to-end data security preservation mechanism, this paper first proposes a grid terminal data security management model based on master-slave Blockchain, including grid terminal, slave Blockchain, and main Blockchain. Among them, the grid terminal mainly completes data generation and data release, the receiving of data and the distributed signature of data are mainly completed from the slave Blockchain, and the main Blockchain mainly completes the intelligent storage of data. Secondly, the data security management mechanism of grid terminal based on master-slave Blockchain is designed, including data distribution process design, data receiving process design, data distributed signature design and data intelligent storage process design. Finally, taking the identity registration and data storage process of the grid terminal as an example, the workflow of the data security management mechanism of the grid terminal based on the master-slave Blockchain is described in detail.

Yang, H., Bai, Y., Zou, Z., Zhang, Q., Wang, B., Yang, R..  2020.  Research on Data Security Sharing Mechanism of Power Internet of Things Based on Blockchain. 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). 9:2029—2032.

The rapid growth of power Internet of Things devices has led to traditional data security sharing mechanisms that are no longer suitable for attribute and permission management of massive devices. In response to this problem, this article proposes a blockchain-based data security sharing mechanism for the power Internet of Things, which reduces the risk of data leakage through decentralization in the architecture and promotes the integration of multiple information and methods.

Tian, Z..  2020.  Design and Implementation of Distributed Government Audit System Based on Multidimensional Online Analysis. 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). :981–983.
With the continuous progress of the information age, e-commerce, the Internet of things and other emerging Internet areas are gradually emerging. Massive amount of structured data auditing becomes a major issue. Log files and other data can be uploaded to the cloud via the Internet to guard against potential threats. Difficulty now is how to realize the data in the field of data audit query online, interactive and impromptu. There are two main methods of data warehouse, respectively is zhang table reduction method and basic data verification method. In the age of big data, data quantity increases gradually, so that the audit speed, design of the data storage and so on will be more or less problematic. If the audit task is not completed in time, it will result in the failure to store the audit data, which will cause losses to enterprises and the government. This paper focuses on the data cube physical model and distributed technical analysis, through the establishment of a set of efficient distributed and online auditing system, so as to make the data fast and efficient auditing.
2021-04-08
Yamaguchi, A., Mizuno, O..  2020.  Reducing Processing Delay and Node Load Using Push-Based Information-Centric Networking. 2020 3rd World Symposium on Communication Engineering (WSCE). :59–63.
Information-Centric Networking (ICN) is attracting attention as a content distribution method against increasing network traffic. Content distribution in ICN adopts a pull-type communication method that returns data to Interest. However, in this case, the push-type communication method is advantageous. Therefore, the authors have proposed a method in which a server pushes content to reduce the node load in an environment where a large amount of Interest to specific content occurs in a short time. In this paper, we analyze the packet processing delay time with and without the proposed method in an environment where a router processes a large number of packets using a simulator. Simulation results show that the proposed method can reduce packet processing delay time and node load.
2021-03-29
Luecking, M., Fries, C., Lamberti, R., Stork, W..  2020.  Decentralized Identity and Trust Management Framework for Internet of Things. 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). :1—9.

Today, Internet of Things (IoT) devices mostly operate in enclosed, proprietary environments. To unfold the full potential of IoT applications, a unifying and permissionless environment is crucial. All IoT devices, even unknown to each other, would be able to trade services and assets across various domains. In order to realize those applications, uniquely resolvable identities are essential. However, quantifiable trust in identities and their authentication are not trivially provided in such an environment due to the absence of a trusted authority. This research presents a new identity and trust framework for IoT devices, based on Distributed Ledger Technology (DLT). IoT devices assign identities to themselves, which are managed publicly and decentralized on the DLT's network as Self Sovereign Identities (SSI). In addition to the Identity Management System (IdMS), the framework provides a Web of Trust (WoT) approach to enable automatic trust rating of arbitrary identities. For the framework we used the IOTA Tangle to access and store data, achieving high scalability and low computational overhead. To demonstrate the feasibility of our framework, we provide a proof-of-concept implementation and evaluate the set objectives for real world applicability as well as the vulnerability against common threats in IdMSs and WoTs.

Khan, S., Jadhav, A., Bharadwaj, I., Rooj, M., Shiravale, S..  2020.  Blockchain and the Identity based Encryption Scheme for High Data Security. 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). :1005—1008.

Using the blockchain technology to store the privatedocuments of individuals will help make data more reliable and secure, preventing the loss of data and unauthorized access. The Consensus algorithm along with the hash algorithms maintains the integrity of data simultaneously providing authentication and authorization. The paper incorporates the block chain and the Identity Based Encryption management concept. The Identity based Management system allows the encryption of the user's data as well as their identity and thus preventing them from Identity theft and fraud. These two technologies combined will result in a more secure way of storing the data and protecting the privacy of the user.

Amin, A. H. M., Abdelmajid, N., Kiwanuka, F. N..  2020.  Identity-of-Things Model using Composite Identity on Permissioned Blockchain Network. 2020 Seventh International Conference on Software Defined Systems (SDS). :171—176.

The growing prevalence of Internet-of-Things (IoT) technology has led to an increase in the development of heterogeneous smart applications. Smart applications may involve a collaborative participation between IoT devices. Participation of IoT devices for specific application requires a tamper-proof identity to be generated and stored, in order to completely represent the device, as well as to eliminate the possibility of identity spoofing and presence of rogue devices in a network. In this paper, we present a composite Identity-of-Things (IDoT) approach on IoT devices with permissioned blockchain implementation for distributed identity management model. Our proposed approach considers both application and device domains in generating the composite identity. In addition, the use of permissioned blockchain for identity storage and verification allows the identity to be immutable. A simulation has been carried out to demonstrate the application of the proposed identity management model.