A Review of Research on Privacy Protection of Internet of Vehicles Based on Blockchain

Numerous academic and industrial fields, such as healthcare, banking, and supply chain management, are rapidly adopting and relying on blockchain technology. It has also been suggested for application in the internet of vehicles (IoV) ecosystem as a way to improve service availability and reliability. Blockchain offers decentralized, distributed and tamper-proof solutions that bring innovation to data sharing and management, but do not themselves protect privacy and data confidentiality. Therefore, solutions using blockchain technology must take user privacy concerns into account. This article reviews the proposed solutions that use blockchain technology to provide different vehicle services while overcoming the privacy leakage problem which inherently exists in blockchain and vehicle services. We analyze the key features and attributes of prior schemes and identify their contributions to provide a comprehensive and critical overview. In addition, we highlight prospective future research topics and present research problems

Improved Attribute-based Encryption with Fpga for Automatic Appliance Control Application in Smart Grid

In this thesis, the author describes the privacy violation issues in smart grid with Automatic Appliance Control applications, and explains the security threats related to it. The smart grid is a sensitive and sophisticated system in real life operation. A mass of data including the remote control commands and users’ energy consumptions is transmitted between the utility companies and other devices in the smart grid such as the substations, smart meters, smart home appliances and much more. Without efficient cryptographic methods, an adversary may hack into the data or the remote control commands and extrapolates a resident’s activity model. Therefore, the Attribute-Based Encryption (ABE) is proposed to provide protection through generating the secret key of user based on a set of attributes which is used to identify different users in the smart grid. And the ciphertext, which is the encrypted remote command, obtains the access policy for decryption. However, ABE algorithm requires long computational time especially a large quantity of attributes are required in a smart grid. The idea of improved ABE system with FPGA is proposed to solve the problem. But the FPGA is only conceptual idea in this thesis and future work of it will be done by other co-worker

An Improved Integrated Hash and Attributed based Encryption Model on High Dimensional Data in Cloud Environment

Cloud computing is a distributed architecture where user can store their private, public or any application software components on it. Many cloud based privacy protection solutions have been implemented, however most of them only focus on limited data resources and storage format. Data confidentiality and inefficient data access methods are the major issues which block the cloud users to store their high dimensional data. With more and more cloud based applications are being available and stored on various cloud servers, a novel multi-user based privacy protection mechanism need to design and develop to improve the privacy protection on high dimensional data. In this paper, a novel integrity algorithm with attribute based encryption model was implemented to ensure confidentiality for high dimensional data security on cloud storage. The main objective of this model is to store, transmit and retrieve the high dimensional cloud data with low computational time and high security. Experimental results show that the proposed model has high data scalability, less computational time and low memory usage compared to traditional cloud based privacy protection models

Vertical Federated Learning:A Structured Literature Review

Federated Learning (FL) has emerged as a promising distributed learning paradigm with an added advantage of data privacy. With the growing interest in having collaboration among data owners, FL has gained significant attention of organizations. The idea of FL is to enable collaborating participants train machine learning (ML) models on decentralized data without breaching privacy. In simpler words, federated learning is the approach of ``bringing the model to the data, instead of bringing the data to the mode''. Federated learning, when applied to data which is partitioned vertically across participants, is able to build a complete ML model by combining local models trained only using the data with distinct features at the local sites. This architecture of FL is referred to as vertical federated learning (VFL), which differs from the conventional FL on horizontally partitioned data. As VFL is different from conventional FL, it comes with its own issues and challenges. In this paper, we present a structured literature review discussing the state-of-the-art approaches in VFL. Additionally, the literature review highlights the existing solutions to challenges in VFL and provides potential research directions in this domain

Taking Computation to Data: Integrating Privacy-preserving AI techniques and Blockchain Allowing Secure Analysis of Sensitive Data on Premise

PhD thesis in Information technologyWith the advancement of artificial intelligence (AI), digital pathology has seen significant progress in recent years. However, the use of medical AI raises concerns about patient data privacy. The CLARIFY project is a research project funded under the European Union’s Marie Sklodowska-Curie Actions (MSCA) program. The primary objective of CLARIFY is to create a reliable, automated digital diagnostic platform that utilizes cloud-based data algorithms and artificial intelligence to enable interpretation and diagnosis of wholeslide-images (WSI) from any location, maximizing the advantages of AI-based digital pathology. My research as an early stage researcher for the CLARIFY project centers on securing information systems using machine learning and access control techniques. To achieve this goal, I extensively researched privacy protection technologies such as federated learning, differential privacy, dataset distillation, and blockchain. These technologies have different priorities in terms of privacy, computational efficiency, and usability. Therefore, we designed a computing system that supports different levels of privacy security, based on the concept: taking computation to data. Our approach is based on two design principles. First, when external users need to access internal data, a robust access control mechanism must be established to limit unauthorized access. Second, it implies that raw data should be processed to ensure privacy and security. Specifically, we use smart contractbased access control and decentralized identity technology at the system security boundary to ensure the flexibility and immutability of verification. If the user’s raw data still cannot be directly accessed, we propose to use dataset distillation technology to filter out privacy, or use locally trained model as data agent. Our research focuses on improving the usability of these methods, and this thesis serves as a demonstration of current privacy-preserving and secure computing technologies

Privacy-Preserving Statistical Analysis of Health Data Using Paillier Homomorphic Encryption and Permissioned Blockchain

Blockchain is a decentralized and peer-to-peer ledger technology that adds transparency, traceability, and immutability to data. It has shown great promise in mitigating the interoperability problem and privacy concerns in the de facto electronic health record anagement systems and has recently received increasing attention from the healthcare industry. Several blockchain-based and decentralized health data management mechanisms have been proposed to improve the quality of care delivery to patients. Apart from care delivery, health data has other important applications, such as education, regulation, research, public health improvement, and policy sup- port. However, existing privacy acts prohibit health institutions and providers from sharing patients\u27 data with third parties. Therefore, research institutions that con- duct research on private health data need a secure system that provides accurate analysis results while preserving patient privacy and minimizing the risks of data breaches. In this thesis, We propose a novel privacy-preserving method for statis- tical analysis of health data. We leveraged the blockchain technology and Paillier encryption algorithm to increase the accuracy of data analysis while preserving the privacy of patients. Smart contracts were used to carry out mathematical operations on the encrypted records in a secure manner. We were able to successfully deploy the proposed scheme on Hyperledger Fabric, a permissioned and consortium blockchain platform. Compared to the previous works, the proposed model enjoys the bene ts of a distributed blockchain-based environment, which include higher availability and enhanced data security. The experimental results show the feasibility of this method with a reasonable amount of time for regular queries. Blockchain is a decentralized and peer-to-peer ledger technology that adds transparency, traceability, and immutability to data. It has shown great promise in mitigating the interoperability problem and privacy concerns in the de facto electronic health record anagement systems and has recently received increasing attention from the healthcare industry. Several blockchain-based and decentralized health data management mechanisms have been proposed to improve the quality of care delivery to patients. Apart from care delivery, health data has other important applications, such as education, regulation, research, public health improvement, and policy sup- port. However, existing privacy acts prohibit health institutions and providers from sharing patients\u27 data with third parties. Therefore, research institutions that con- duct research on private health data need a secure system that provides accurate analysis results while preserving patient privacy and minimizing the risks of data breaches. In this thesis, We propose a novel privacy-preserving method for statis- tical analysis of health data. We leveraged the blockchain technology and Paillier encryption algorithm to increase the accuracy of data analysis while preserving the privacy of patients. Smart contracts were used to carry out mathematical operations on the encrypted records in a secure manner. We were able to successfully deploy the proposed scheme on Hyperledger Fabric, a permissioned and consortium blockchain platform. Compared to the previous works, the proposed model enjoys the bene ts of a distributed blockchain-based environment, which include higher availability and enhanced data security. The experimental results show the feasibility of this method with a reasonable amount of time for regular queries

Multiple Authorities Access under Public Cloud Storage: Review

Public cloud storage is a cloud storage model that provide services to individuals and organizations to store, edit and manage data. Public cloud storage service is also known as storage service, utility storage and online storage. Cloud storage has many advantages, there is still remain various challenges among which privacy and security of users data have major issues in public cloud storage. Attribute Based Encryption(ABE) is a cryptographic technique which provides data owner direct control over their data in public cloud storage. In the traditional ABE scheme involve only one authority to maintain attribute set which can bring a single-point bottleneck on security and performance. Now we use threshold multi-authority Cipher text-Policy Attribute-Based Encryption (CP-ABE) access control scheme, name TMACS. TMACS is Threshold Multi-Authority Access Control System. In TMACS, multiple authority jointly manages the whole attribute set but no user has full control of any specific attribute. By combining threshold secret sharing (t,n) and multi-authority CP-ABE scheme, we developed efficient multi-authority access control system in public cloud storage

Towards Cyber Security for Low-Carbon Transportation: Overview, Challenges and Future Directions

In recent years, low-carbon transportation has become an indispensable part as sustainable development strategies of various countries, and plays a very important responsibility in promoting low-carbon cities. However, the security of low-carbon transportation has been threatened from various ways. For example, denial of service attacks pose a great threat to the electric vehicles and vehicle-to-grid networks. To minimize these threats, several methods have been proposed to defense against them. Yet, these methods are only for certain types of scenarios or attacks. Therefore, this review addresses security aspect from holistic view, provides the overview, challenges and future directions of cyber security technologies in low-carbon transportation. Firstly, based on the concept and importance of low-carbon transportation, this review positions the low-carbon transportation services. Then, with the perspective of network architecture and communication mode, this review classifies its typical attack risks. The corresponding defense technologies and relevant security suggestions are further reviewed from perspective of data security, network management security and network application security. Finally, in view of the long term development of low-carbon transportation, future research directions have been concerned.Comment: 34 pages, 6 figures, accepted by journal Renewable and Sustainable Energy Review