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2021-10-12
Suharsono, Teguh Nurhadi, Anggraini, Dini, Kuspriyanto, Rahardjo, Budi, Gunawan.  2020.  Implementation of Simple Verifiability Metric to Measure the Degree of Verifiability of E-Voting Protocol. 2020 14th International Conference on Telecommunication Systems, Services, and Applications (TSSA. :1–3.
Verifiability is one of the parameters in e-voting that can increase confidence in voting technology with several parties ensuring that voters do not change their votes. Voting has become an important part of the democratization system, both to make choices regarding policies, to elect representatives to sit in the representative assembly, and to elect leaders. the more voters and the wider the distribution, the more complex the social life, and the need to manage the voting process efficiently and determine the results more quickly, electronic-based voting (e-Voting) is becoming a more promising option. The level of confidence in voting depends on the capabilities of the system. E-voting must have parameters that can be used as guidelines, which include the following: Accuracy, Invulnerability, Privacy and Verifiability. The implementation of the simple verifiability metric to measure the degree of verifiability in the e-voting protocol, the researchers can calculate the degree of verifiability in the e-voting protocol and the researchers have been able to assess the proposed e-voting protocol with the standard of the best degree of verifiability is 1, where the value of 1 is is absolutely verified protocol.
Uy, Francis Aldrine A., Vea, Larry A., Binag, Matthew G., Diaz, Keith Anshilo L., Gallardo, Roy G., Navarro, Kevin Jorge A., Pulido, Maria Teresa R., Pinca, Ryan Christopher B., Rejuso, Billy John Rudolfh I., Santos, Carissa Jane R..  2020.  The Potential of New Data Sources in a Data-Driven Transportation, Operation, Management and Assessment System (TOMAS). 2020 IEEE Conference on Technologies for Sustainability (SusTech). :1–8.
We present our journey in constructing the first integrated data warehouse for Philippine transportation research in the hopes of developing a Transportation Decision Support System for impact studies and policy making. We share how we collected data from diverse sources, processed them into a homogeneous format and applied them to our multimodal platform. We also list the challenges we encountered, including bureaucratic delays, data privacy concerns, lack of software, and overlapping datasets. The data warehouse shall serve as a public resource for researchers and professionals, and for government officials to make better-informed policies. The warehouse will also function within our multi-modal platform for measurement, modelling, and visualization of road transportation. This work is our contribution to improve the transportation situation in the Philippines, both in the local and national levels, to boost our economy and overall quality of life.
Jayabalan, Manoj.  2020.  Towards an Approach of Risk Analysis in Access Control. 2020 13th International Conference on Developments in eSystems Engineering (DeSE). :287–292.
Information security provides a set of mechanisms to be implemented in the organisation to protect the disclosure of data to the unauthorised person. Access control is the primary security component that allows the user to authorise the consumption of resources and data based on the predefined permissions. However, the access rules are static in nature, which does not adapt to the dynamic environment includes but not limited to healthcare, cloud computing, IoT, National Security and Intelligence Arena and multi-centric system. There is a need for an additional countermeasure in access decision that can adapt to those working conditions to assess the threats and to ensure privacy and security are maintained. Risk analysis is an act of measuring the threats to the system through various means such as, analysing the user behaviour, evaluating the user trust, and security policies. It is a modular component that can be integrated into the existing access control to predict the risk. This study presents the different techniques and approaches applied for risk analysis in access control. Based on the insights gained, this paper formulates the taxonomy of risk analysis and properties that will allow researchers to focus on areas that need to be improved and new features that could be beneficial to stakeholders.
Yang, Howard H., Arafa, Ahmed, Quek, Tony Q. S., Vincent Poor, H..  2020.  Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :8743–8747.
Federated learning (FL) is a machine learning model that preserves data privacy in the training process. Specifically, FL brings the model directly to the user equipments (UEs) for local training, where an edge server periodically collects the trained parameters to produce an improved model and sends it back to the UEs. However, since communication usually occurs through a limited spectrum, only a portion of the UEs can update their parameters upon each global aggregation. As such, new scheduling algorithms have to be engineered to facilitate the full implementation of FL. In this paper, based on a metric termed the age of update (AoU), we propose a scheduling policy by jointly accounting for the staleness of the received parameters and the instantaneous channel qualities to improve the running efficiency of FL. The proposed algorithm has low complexity and its effectiveness is demonstrated by Monte Carlo simulations.
Adibi, Mahya, van der Woude, Jacob.  2020.  Distributed Learning Control for Economic Power Dispatch: A Privacy Preserved Approach*. 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE). :821–826.
We present a privacy-preserving distributed reinforcement learning-based control scheme to address the problem of frequency control and economic dispatch in power generation systems. The proposed control approach requires neither a priori system model knowledge nor the mathematical formulation of the generation cost functions. Due to not requiring the generation cost models, the control scheme is capable of dealing with scenarios in which the cost functions are hard to formulate and/or non-convex. Furthermore, it is privacy-preserving, i.e. none of the units in the network needs to communicate its cost function and/or control policy to its neighbors. To realize this, we propose an actor-critic algorithm with function approximation in which the actor step is performed individually by each unit with no need to infer the policies of others. Moreover, in the critic step each generation unit shares its estimate of the local measurements and the estimate of its cost function with the neighbors, and via performing a consensus algorithm, a consensual estimate is achieved. The performance of our proposed control scheme, in terms of minimizing the overall cost while persistently fulfilling the demand and fast reaction and convergence of our distributed algorithm, is demonstrated on a benchmark case study.
Sethi, Kamalakanta, Pradhan, Ankit, Bera, Padmalochan.  2020.  Attribute-Based Data Security with Obfuscated Access Policy for Smart Grid Applications. 2020 International Conference on COMmunication Systems NETworkS (COMSNETS). :503–506.
Smart grid employs intelligent transmission and distribution networks for effective and reliable delivery of electricity. It uses fine-grained electrical measurements to attain optimized reliability and stability by sharing these measurements among different entities of energy management systems of the grid. There are many stakeholders like users, phasor measurement units (PMU), and other entities, with changing requirements involved in the sharing of the data. Therefore, data security plays a vital role in the correct functioning of a power grid network. In this paper, we propose an attribute-based encryption (ABE) for secure data sharing in Smart Grid architectures as ABE enables efficient and secure access control. Also, the access policy is obfuscated to preserve privacy. We use Linear Secret Sharing (LSS) Scheme for supporting any monotone access structures, thereby enhancing the expressiveness of access policies. Finally, we also analyze the security, access policy privacy and collusion resistance properties along with efficiency analysis of our cryptosystem.
Ferraro, Angelo.  2020.  When AI Gossips. 2020 IEEE International Symposium on Technology and Society (ISTAS). :69–71.
The concept of AI Gossip is presented. It is analogous to the traditional understanding of a pernicious human failing. It is made more egregious by the technology of AI, internet, current privacy policies, and practices. The recognition by the technological community of its complacency is critical to realizing its damaging influence on human rights. A current example from the medical field is provided to facilitate the discussion and illustrate the seriousness of AI Gossip. Further study and model development is encouraged to support and facilitate the need to develop standards to address the implications and consequences to human rights and dignity.
Chang, Kai Chih, Nokhbeh Zaeem, Razieh, Barber, K. Suzanne.  2020.  Is Your Phone You? How Privacy Policies of Mobile Apps Allow the Use of Your Personally Identifiable Information 2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :256–262.
People continue to store their sensitive information in their smart-phone applications. Users seldom read an app's privacy policy to see how their information is being collected, used, and shared. In this paper, using a reference list of over 600 Personally Identifiable Information (PII) attributes, we investigate the privacy policies of 100 popular health and fitness mobile applications in both Android and iOS app markets to find the set of personal information these apps collect, use and share. The reference list of PII was independently built from a longitudinal study at The University of Texas investigating thousands of identity theft and fraud cases where PII attributes and associated value and risks were empirically quantified. This research leverages the reference PII list to identify and analyze the value of personal information collected by the mobile apps and the risk of disclosing this information. We found that the set of PII collected by these mobile apps covers 35% of the entire reference set of PII and, due to dependencies between PII attributes, these mobile apps have a likelihood of indirectly impacting 70% of the reference PII if breached. For a specific app, we discovered the monetary loss could reach \$1M if the set of sensitive data it collects is breached. We finally utilize Bayesian inference to measure risks of a set of PII gathered by apps: the probability that fraudsters can discover, impersonate and cause harm to the user by misusing only the PII the mobile apps collected.
Liao, Guocheng, Chen, Xu, Huang, Jianwei.  2020.  Privacy Policy in Online Social Network with Targeted Advertising Business. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :934–943.
In an online social network, users exhibit personal information to enjoy social interaction. The social network provider (SNP) exploits users' information for revenue generation through targeted advertising. The SNP can present ads to proper users efficiently. Therefore, an advertiser is more willing to pay for targeted advertising. However, the over-exploitation of users' information would invade users' privacy, which would negatively impact users' social activeness. Motivated by this, we study the optimal privacy policy of the SNP with targeted advertising business. We characterize the privacy policy in terms of the fraction of users' information that the provider should exploit, and formulate the interactions among users, advertiser, and SNP as a three-stage Stackelberg game. By carefully leveraging supermodularity property, we reveal from the equilibrium analysis that higher information exploitation will discourage users from exhibiting information, lowering the overall amount of exploited information and harming advertising revenue. We further characterize the optimal privacy policy based on the connection between users' information levels and privacy policy. Numerical results reveal some useful insights that the optimal policy can well balance the users' trade-off between social benefit and privacy loss.
Faurie, Pascal, Moldovan, Arghir-Nicolae, Tal, Irina.  2020.  Privacy Policy – ``I Agree''⁈ – Do Alternatives to Text-Based Policies Increase the Awareness of the Users? 2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1–6.
Since GDPR was introduced, there is a reinforcement of the fact that users must give their consent before their personal data can be managed by any website. However, many studies have demonstrated that users often skip these policies and click the "I agree" button to continue browsing, being unaware of what the consent they gave was about, hence defeating the purpose of GDPR. This paper investigates if different ways of presenting users the privacy policy can change this behaviour and can lead to an increased awareness of the user in relation to what the user agrees with. Three different types of policies were used in the study: a full-text policy, a so-called usable policy, and a video-based policy. Results demonstrated that the type of policy has a direct influence on the user awareness and user satisfaction. The two alternatives to the text-based policy lead to a significant increase of user awareness in relation to the content of the policy and to a significant increase in the user satisfaction in relation to the usability of the policy.
Farooq, Emmen, Nawaz UI Ghani, M. Ahmad, Naseer, Zuhaib, Iqbal, Shaukat.  2020.  Privacy Policies' Readability Analysis of Contemporary Free Healthcare Apps. 2020 14th International Conference on Open Source Systems and Technologies (ICOSST). :1–7.
mHealth apps have a vital role in facilitation of human health management. Users have to enter sensitive health related information in these apps to fully utilize their functionality. Unauthorized sharing of sensitive health information is undesirable by the users. mHealth apps also collect data other than that required for their functionality like surfing behavior of a user or hardware details of devices used. mHealth software and their developers also share such data with third parties for reasons other than medical support provision to the user, like advertisements of medicine and health insurance plans. Existence of a comprehensive and easy to understand data privacy policy, on user data acquisition, sharing and management is a salient requirement of modern user privacy protection demands. Readability is one parameter by which ease of understanding of privacy policy is determined. In this research, privacy policies of 27 free Android, medical apps are analyzed. Apps having user rating of 4.0 and downloads of 1 Million or more are included in data set of this research.RGL, Flesch-Kincaid Reading Grade Level, SMOG, Gunning Fox, Word Count, and Flesch Reading Ease of privacy policies are calculated. Average Reading Grade Level of privacy policies is 8.5. It is slightly greater than average adult RGL in the US. Free mHealth apps have a large number of users in other, less educated parts of the World. Privacy policies with an average RGL of 8.5 may be difficult to comprehend in less educated populations.
Al Omar, Abdullah, Jamil, Abu Kaisar, Nur, Md. Shakhawath Hossain, Hasan, Md Mahamudul, Bosri, Rabeya, Bhuiyan, Md Zakirul Alam, Rahman, Mohammad Shahriar.  2020.  Towards A Transparent and Privacy-Preserving Healthcare Platform with Blockchain for Smart Cities. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1291–1296.
In smart cities, data privacy and security issues of Electronic Health Record(EHR) are grabbing importance day by day as cyber attackers have identified the weaknesses of EHR platforms. Besides, health insurance companies interacting with the EHRs play a vital role in covering the whole or a part of the financial risks of a patient. Insurance companies have specific policies for which patients have to pay them. Sometimes the insurance policies can be altered by fraudulent entities. Another problem that patients face in smart cities is when they interact with a health organization, insurance company, or others, they have to prove their identity to each of the organizations/companies separately. Health organizations or insurance companies have to ensure they know with whom they are interacting. To build a platform where a patient's personal information and insurance policy are handled securely, we introduce an application of blockchain to solve the above-mentioned issues. In this paper, we present a solution for the healthcare system that will provide patient privacy and transparency towards the insurance policies incorporating blockchain. Privacy of the patient information will be provided using cryptographic tools.
Martiny, Karsten, Denker, Grit.  2020.  Partial Decision Overrides in a Declarative Policy Framework. 2020 IEEE 14th International Conference on Semantic Computing (ICSC). :271–278.
The ability to specify various policies with different overriding criteria allows for complex sets of sharing policies. This is particularly useful in situations in which data privacy depends on various properties of the data, and complex policies are needed to express the conditions under which data is protected. However, if overriding policy decisions constrain the affected data, decisions from overridden policies should not be suppressed completely, because they can still apply to subsets of the affected data. This article describes how a privacy policy framework can be extended with a mechanism to partially override decisions based on specified constraints. Our solution automatically generates complementary sets of decisions for both the overridden and the complementary, non-overridden subsets of the data, and thus, provides a means to specify a complex policies tailored to specific properties of the protected data.
Zaeem, Razieh Nokhbeh, Anya, Safa, Issa, Alex, Nimergood, Jake, Rogers, Isabelle, Shah, Vinay, Srivastava, Ayush, Barber, K. Suzanne.  2020.  PrivacyCheck's Machine Learning to Digest Privacy Policies: Competitor Analysis and Usage Patterns. 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). :291–298.
Online privacy policies are lengthy and hard to comprehend. To address this problem, researchers have utilized machine learning (ML) to devise tools that automatically summarize online privacy policies for web users. One such tool is our free and publicly available browser extension, PrivacyCheck. In this paper, we enhance PrivacyCheck by adding a competitor analysis component-a part of PrivacyCheck that recommends other organizations in the same market sector with better privacy policies. We also monitored the usage patterns of about a thousand actual PrivacyCheck users, the first work to track the usage and traffic of an ML-based privacy analysis tool. Results show: (1) there is a good number of privacy policy URLs checked repeatedly by the user base; (2) the users are particularly interested in privacy policies of software services; and (3) PrivacyCheck increased the number of times a user consults privacy policies by 80%. Our work demonstrates the potential of ML-based privacy analysis tools and also sheds light on how these tools are used in practice to give users actionable knowledge they can use to pro-actively protect their privacy.
Tavakolan, Mona, Faridi, Ismaeel A..  2020.  Applying Privacy-Aware Policies in IoT Devices Using Privacy Metrics. 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI). :1–5.
In recent years, user's privacy has become an important aspect in the development of Internet of Things (IoT) devices. However, there has been comparatively little research so far that aims to understanding user's privacy in connection with IoT. Many users are worried about protecting their personal information, which may be gathered by IoT devices. In this paper, we present a new method for applying the user's preferences within the privacy-aware policies in IoT devices. Users can prioritize a set of extendable privacy policies based on their preferences. This is achieved by assigning weights to these policies to form ranking criteria. A privacy-aware index is then calculated based on these ranking. In addition, IoT devices can be clustered based on their privacy-aware index value. In this paper, we present a new method for applying the user's preferences within the privacy-aware policies in IoT devices. Users can prioritize a set of extendable privacy policies based on their preferences. This is achieved by assigning weights to these policies to form ranking criteria. A privacy-aware index is then calculated based on these ranking. In addition, IoT devices can be clustered based on their privacy-aware index value.
Onu, Emmanuel, Mireku Kwakye, Michael, Barker, Ken.  2020.  Contextual Privacy Policy Modeling in IoT. 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :94–102.
The Internet of Things (IoT) has been one of the biggest revelations of the last decade. These cyber-physical systems seamlessly integrate and improve the activities in our daily lives. Hence, creating a wide application for it in several domains, such as smart buildings and cities. However, the integration of IoT also comes with privacy challenges. The privacy challenges result from the ability of these devices to pervasively collect personal data about individuals through sensors in ways that could be unknown to them. A number of research efforts have evaluated privacy policy awareness and enforcement as key components for addressing these privacy challenges. This paper provides a framework for understanding contextualized privacy policy within the IoT domain. This will enable IoT privacy researchers to better understand IoT privacy policies and their modeling.
2021-10-04
Jungum, Nevin Vunka, Mohamudally, Nawaz, Nissanke, Nimal.  2020.  Device Selection Decision Making using Multi-Criteria for Offloading Application Mobile Codes. 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). :326–331.
With fast growing research in the area of application partitioning for offloading, determining which devices to prioritize over the other for mobile code offloading is fundamental. Multiple methods can be adopted using both single-criterion and multiple-criteria strategies. Due to the characteristics of pervasive environments, whereby devices having different computing capability, different level of privacy and security and the mobility nature in such environment makes the decision-making process complex. To this end, this paper proposes a method using a combination of the method Analytic Hierarchy Process (AHP) to calculate weights criteria of participating devices. Next the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) is considered to sort in order of priority the participating devices, hence facilitating the decision to opt for which participating device first. An evaluation of the method is also presented.
Ghorashi, Seyed Ramin, Zia, Tanveer, Jiang, Yinhao.  2020.  Optimisation of Lightweight Klein Encryption Algorithm With 3 S-box. 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). :1–5.
Internet of Things (IoT) have offered great opportunities for the growth of smart objects in the last decade. Smart devices are deployed in many fields such as smart cities, healthcare and agriculture. One of the applications of IoT is Wireless Sensor Networks (WSN) that require inexpensive and space-economic design for remote sensing and communication capabilities. This, unfortunately, lead to their inherent security vulnerabilities. Lightweight cryptography schemes are designed to counter many attacks in low-powered devices such as the IoT and WSN. These schemes can provide support for data encryption and key management while maintaining some level of efficiency. Most of these block ciphers provide good security. However, due to the complex cryptographic scheme's efficiency and optimisation is an issue. In this work, we focus on a new lightweight encryption scheme called the Klein block cipher. The algorithms of Klein block cipher are analysed for performance and security optimisations. A new algorithm which consists of 3-layer substitute box is proposed to reduce the need for resource consumption but maintain the security.
Abbas Hamdani, Syed Wasif, Waheed Khan, Abdul, Iltaf, Naima, Iqbal, Waseem.  2020.  DTMSim-IoT: A Distributed Trust Management Simulator for IoT Networks. 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :491–498.
In recent years, several trust management frame-works and models have been proposed for the Internet of Things (IoT). Focusing primarily on distributed trust management schemes; testing and validation of these models is still a challenging task. It requires the implementation of the proposed trust model for verification and validation of expected outcomes. Nevertheless, a stand-alone and standard IoT network simulator for testing of distributed trust management scheme is not yet available. In this paper, a .NET-based Distributed Trust Management Simulator for IoT Networks (DTMSim-IoT) is presented which enables the researcher to implement any static/dynamic trust management model to compute the trust value of a node. The trust computation will be calculated based on the direct-observation and trust value is updated after every transaction. Transaction history and logs of each event are maintained which can be viewed and exported as .csv file for future use. In addition to that, the simulator can also draw a graph based on the .csv file. Moreover, the simulator also offers to incorporate the feature of identification and mitigation of the On-Off Attack (OOA) in the IoT domain. Furthermore, after identifying any malicious activity by any node in the networks, the malevolent node is added to the malicious list and disseminated in the network to prevent potential On-Off attacks.
Zhang, Chong, Liu, Xiao, Zheng, Xi, Li, Rui, Liu, Huai.  2020.  FengHuoLun: A Federated Learning based Edge Computing Platform for Cyber-Physical Systems. 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). :1–4.
Cyber-Physical Systems (CPS) such as intelligent connected vehicles, smart farming and smart logistics are constantly generating tons of data and requiring real-time data processing capabilities. Therefore, Edge Computing which provisions computing resources close to the End Devices from the network edge is becoming the ideal platform for CPS. However, it also brings many issues and one of the most prominent challenges is how to ensure the development of trustworthy smart services given the dynamic and distributed nature of Edge Computing. To tackle this challenge, this paper proposes a novel Federated Learning based Edge Computing platform for CPS, named “FengHuoLun”. Specifically, based on FengHuoLun, we can: 1) implement smart services where machine learning models are trained in a trusted Federated Learning framework; 2) assure the trustworthiness of smart services where CPS behaviours are tested and monitored using the Federated Learning framework. As a work in progress, we have presented an overview of the FengHuoLun platform and also some preliminary studies on its key components, and finally discussed some important future research directions.
Mohiuddin, Irfan, Almogren, Ahmad.  2020.  Security Challenges and Strategies for the IoT in Cloud Computing. 2020 11th International Conference on Information and Communication Systems (ICICS). :367–372.
The Internet of Things is progressively turning into a pervasive computing service, needing enormous volumes of data storage and processing. However, due to the distinctive properties of resource constraints, self-organization, and short-range communication in Internet of Things (IoT), it always adopts to cloud for outsourced storage and computation. This integration of IoT with cloud has a row of unfamiliar security challenges for the data at rest. Cloud computing delivers highly scalable and flexible computing and storage resources on pay-per-use policy. Cloud computing services for computation and storage are getting increasingly popular and many organizations are now moving their data from in-house data centers to the Cloud Storage Providers (CSPs). Time varying workload and data intensive IoT applications are vulnerable to encounter challenges while using cloud computing services. Additionally, the encryption techniques and third-party auditors to maintain data integrity are still in their developing stage and therefore the data at rest is still a concern for IoT applications. In this paper, we perform an analysis study to investigate the challenges and strategies adapted by Cloud Computing to facilitate a safe transition of IoT applications to the Cloud.
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.
Sallal, Muntadher, Owenson, Gareth, Adda, Mo.  2020.  Security and Performance Evaluation of Master Node Protocol in the Bitcoin Peer-to-Peer Network. 2020 IEEE Symposium on Computers and Communications (ISCC). :1–6.
This paper proposes a proximity-aware extensions to the current Bitcoin protocol, named Master Node Based Clustering (MNBC). The ultimate purpose of the proposed protocol is to evaluate the security and performance of grouping nodes based on physical proximity. In MNBC protocol, physical internet connectivity increases as well as the number of hops between nodes decreases through assigning nodes to be responsible for propagating based on physical internet proximity.
Das, Debashis, Banerjee, Sourav, Mansoor, Wathiq, Biswas, Utpal, Chatterjee, Pushpita, Ghosh, Uttam.  2020.  Design of a Secure Blockchain-Based Smart IoV Architecture. 2020 3rd International Conference on Signal Processing and Information Security (ICSPIS). :1–4.
Blockchain is developing rapidly in various domains for its security. Nowadays, one of the most crucial fundamental concerns is internet security. Blockchain is a novel solution to enhance the security of network applications. However, there are no precise frameworks to secure the Internet of Vehicle (IoV) using Blockchain technology. In this paper, a blockchain-based smart internet of vehicle (BSIoV) framework has been proposed due to the cooperative, collaborative, transparent, and secure characteristics of Blockchain. The main contribution of the proposed work is to connect vehicle-related authorities together to fix a secure and transparent vehicle-to-everything (V2X) communication through the peer-to-peer network connection and provide secure services to the intelligent transport systems. A key management strategy has been included to identify a vehicle in this proposed system. The proposed framework can also provide a significant solution for the data security and safety of the connected vehicles in blockchain network.
Ding, Lei, Wang, Shida, Wan, Renzhuo, Zhou, Guopeng.  2020.  Securing core information sharing and exchange by blockchain for cooperative system. 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS). :579–583.
The privacy protection and information security are two crucial issues for future advanced artificial intelligence devices, especially for cooperative system with rich core data exchange which may offer opportunities for attackers to fake interaction messages. To combat such threat, great efforts have been made by introducing trust mechanism in initiative or passive way. Furthermore, blockchain and distributed ledger technology provide a decentralized and peer-to-peer network, which has great potential application for multi-agent system, such as IoTs and robots. It eliminates third-party interference and data in the blockchain are stored in an encrypted way permanently and anti-destroys. In this paper, a methodology of blockchain is proposed and designed for advanced cooperative system with artificial intelligence to protect privacy and sensitive data exchange between multi-agents. The validation procedure is performed in laboratory by a three-level computing networks of Raspberry Pi 3B+, NVIDIA Jetson Tx2 and local computing server for a robot system with four manipulators and four binocular cameras in peer computing nodes by Go language.