Query: Data Integrity

title: Attribute Based Cloud Data Integrity Auditing for Secure Outsourced Storage
abstract: Outsourced storage such as cloud storage can significantly reduce the burden of data management of data owners. Despite of a long list of merits of cloud storage, it triggers many security risks at the same time. Data integrity, one of the most burning challenges in secure cloud storage, is a fundamental and pivotal element in outsourcing services. Outsourced data auditing protocols enable a verifier to efficiently check the integrity of the outsourced files without downloading the entire file from the cloud, which can dramatically reduce the communication overhead between the cloud server and the verifier. Existing protocols are mostly based on public key infrastructure or an exact identity, which lacks flexibility of key management. In this paper, we seek to address the complex key management challenge in cloud data integrity checking by introducing attribute based cloud data auditing, where users can upload files to cloud through some customized attribute set and specify some designated auditor set to check the integrity of the outsourced data. We formalize the system model and the security model for this new primitive, and describe a concrete construction of attribute based cloud data integrity auditing protocol. The new protocol offers desirable properties namely attribute privacy preserving and collusion resistance. We prove soundness of our protocol based on the computational Diffie Hellman assumption and the discrete logarithm assumption. Finally, we develop a prototype of the protocol which demonstrates the practicality of the protocol.

title: Preserving Balance Between Privacy and Data Integrity in Edge Assisted Internet of Things
abstract: Internet of Things (IoT) devices and the edge jointly broaden the IoT's sensing capability and the monitoring scope for various applications. Though accessing sensing data and making decisions through IoT smart devices turns out to be commonplace, it is challenging to guarantee user privacy and preserve the accuracy (integrity) of the collected data. The IoT smart devices frequently lose either IoT user's privacy or data integrity. This also makes it crucial to put a threshold on the cost of computation and load of the IoT devices, as gradually more IoT services demand access to the resources that devices offer. In this article, we propose BalancePIC, a scheme that attempts to preserve a balance in the three aspects (user privacy, data integrity in edge assisted IoT devices, and the computational cost) It achieves the balance through a balanced truth discovery approach and a proposed enhanced technique for data privacy, which are used in IoT devices and edge server interactions. It authenticates the IoT user participation with privacy in the truth discovery process through a biometric ECC based authentication algorithm. The nature of the BalancePIC scheme is to straightforwardly provide the likelihood for a simple amendment on the cryptography technique and weight assignment. This lessens the overall computational cost for the IoT user devices but also restricts the communications between the user devices and the edge server, which is important for data integrity. We present an enhanced technique to preserve privacy by guarding the user from potential threats and suspicious data collection parties. To achieve this, BalancePIC takes steps to blur the original sensory data of the device by processing results in groups called zones. Simulation result analysis provides evidence for the balance preservation in the three aspects.

title: Blockchain based privacy preserving remote data integrity checking scheme for IoT information systems
abstract: Remote data integrity checking is of great importance to the security of cloud based information systems. Previous works generally assume a trusted third party to oversee the integrity of the outsourced data, which may be invalid in practice. In this paper, we utilize the blockchain to construct a novel privacy preserving remote data integrity checking scheme for Internet of Things (IoT) information management systems without involving trusted third parties. Our scheme leverages the Lifted EC ElGamal cryptosystem, bilinear pairing, and blockchain to support efficient public batch signature verifications and protect the security and data privacy of the IoT systems. The results of the experiment demonstrate the efficiency of our scheme.

title: Privacy preserving data integrity verification by using lightweight streaming authenticated data structures for healthcare cyber physical system
abstract: Nowadays the healthcare system is undergoing a paradigm shift to integrate cloud computing and Internet of Things with physical medical equipments into a distributed network ensuring real time and near real time data transfer from the physical world to the cyber space for computation, storage and analysis. Namely, the healthcare system can be seen as a cyber physical system (CPS) for patient centric healthcare applications and services, called Health CPS, based on cloud computing, Wireless Body Area Networks (WBANs) and big data analytics technologies. Health CPS has played an important role in healthcare for improving treatment quality and patients' assistance speed. However, despite these advantages, the development of Health CPS will be restricted by serious security threats, especially the security threats to the healthcare related data. Because Health CPS forward Electronic health records (EHRs) biomedical signals of patients and public health to the cloud which may operate in distributed and hostile environments, novel security mechanisms are required to prevent malicious interactions to the storage infrastructure. Therefore, the cloud providers must take strong security measures to protect the integrity and privacy of the healthcare related data. But most of the researchers do not simultaneously pay attention to both integrity and privacy for Health CPS. Therefore, we proposed a privacy preserving data integrity verification model by using lightweight streaming authenticated data structures for Health CPS. We have given the design idea, architecture, formal definition, security definition, communication protocols of our model in detail. The key construction processes of our model which include initialization, data appending, scale expansion, data query and verification, are also given in this paper. Finally, the security and performance analysis show that our scheme is not only secure but also efficient.

title: Blockchain data based cloud data integrity protection mechanism
abstract: Despite the rapid development of cloud computing for many years, data security and trusted computing are still the main challenges in current cloud computing applications. In order to solve this problem, many scholars have carried out a lot of research on this, and proposed many models including data integrity test and secure multi party calculation. However, most of these solutions face problems such as excessive computational complexity or lack of scalability. This paper studies the use of blockchain techniques to improve this situation. Blockchain is a decentralized new distributed computing paradigm. Applying blockchain technology to cloud computing, using the security mechanism of the former to improve the performance of the latter's secure storage and secure computing is a promising research topic. In this paper, the distributed virtual machine agent model is deployed in the cloud by using mobile agent technology. The virtual machine agent enables multi tenants to cooperate with each other to ensure data trust verification. The tasks of reliable data storage, monitoring and verification are completed by virtual machine agent mechanism. This is also a necessary condition for building a blockchain integrity protection mechanism. The blockchain based integrity protection framework is built by the virtual machine proxy model, and the unique hash value corresponding to the file generated by the Merkel hash tree is used to monitor the data change by means of the smart contract on the blockchain, and the data is owned in time. The user issues a warning message for data tampering; in addition, a "block and response" mode is used to construct a blockchain based cloud data integrity verification scheme.

title: Stochastic Blockchain for IoT Data Integrity
abstract: This paper proposes a blockchain based data checking scheme to protect data integrity in Internet of Things (IoT) Traditional data integrity schemes such as symmetric key approaches and public key infrastructure (PKI) suffer from the single point of failure and network congestion due to the centralized architecture. Motivated by the distributed data authentication in blockchain, we propose to adopt blockchain to ensure the data integrity in IoT networks. However, the existing blockchain scheme cannot be directly applied to IoT nodes with limited computing and network resources. Hence, we develop a stochastic blockchain scheme to limit the number of cooperative nodes and distribute the load to IoT edge nodes. In our scheme, the IoT data are broadcast by randomly selected cooperative nodes, thereby introducing uncertainty to the attacker and improving the system security level. Finally, we propose a lightweight mining process to make only the IoT edge nodes compete for block generation and share the block with other nodes. If our scheme is used in the case of having 9,000 legitimate nodes and 1,000 compromised nodes, only three cooperative nodes can achieve the probability of successful defense over 99 percent.

title: Fuzzy Identity Based Data Integrity Auditing for Reliable Cloud Storage Systems
abstract: Data integrity, a core security issue in reliable cloud storage, has received much attention. Data auditing protocols enable a verifier to efficiently check the integrity of the outsourced data without downloading the data. A key research challenge associated with existing designs of data auditing protocols is the complexity in key management. In this paper, we seek to address the complex key management challenge in cloud data integrity checking by introducing fuzzy identity based auditing, the first in such an approach, to the best of our knowledge. More specifically, we present the primitive of fuzzy identity based data auditing, where a user's identity can be viewed as a set of descriptive attributes. We formalize the system model and the security model for this new primitive. We then present a concrete construction of fuzzy identity based auditing protocol by utilizing biometrics as the fuzzy identity. The new protocol offers the property of error tolerance, namely, it binds with private key to one identity which can be used to verify the correctness of a response generated with another identity, if and only if both identities are sufficiently close. We prove the security of our protocol based on the computational Diffie Hellman assumption and the discrete logarithm assumption in the selective ID security model. Finally, we develop a prototype implementation of the protocol which demonstrates the practicality of the proposal.

title: Key Issues in Healthcare Data Integrity: Analysis and Recommendations
abstract: Managing data integrity is a challenging task for any expert or a researcher. This study attempts to collate a Systematic Literature Review of the research efforts done in the domain of healthcare data integrity. The paper highlights the criticalness of data integrity issues in healthcare through attack statistics in the first section. The second section of the paper systematically reviews the previous studies discussing the healthcare related Systematic literature reviews and data integrity techniques in healthcare sector. The third section of this study examines the collated literature through various analysis methodologies and discusses about the most prioritized technique as well as its challenges in healthcare data security. The fourth section illustrates about the various challenges and future directions to take while constructing a roadmap for the future research endeavors in healthcare data integrity management techniques. The concluding segment of the paper presents an objective assessment and sensitivity analysis for finding the implications and difficulties in the studies while outlining feasible solutions. Furthermore, this research endeavour also conducts a Scientometric analysis of all the studies for better understanding of the literature reviewed. Ranking or the Priority analysis part of the paper is totally dedicated to the previously used techniques in healthcare. The paper also discusses about data integrity techniques and postulates that the most prioritized data integrity technique is the blockchain.

title: Unsupervised Machine Learning Based Detection of Covert Data Integrity Assault in Smart Grid Networks Utilizing Isolation Forest
abstract: Being one of the most multifaceted cyber physical systems, smart grids (SGs) are arguably more prone to cyber threats. A covert data integrity assault (CDIA) on a communications network may be lethal to the reliability and safety of SG operations. They are intelligently designed to sidestep the traditional bad data detector in power control centers, and this type of assault can compromise the integrity of the data, causing a false estimation of the state that further severely distresses the entire power system operation. In this paper, we propose an unsupervised machine learning based scheme to detect CDIAs in SG communications networks utilizing non labeled data. The proposed scheme employs a state of the art algorithm, called isolation forest, and detects CDIAs based on the hypothesis that the assault has the shortest average path length in a constructed random forest. To tackle the dimensionality issue from the growth in power systems, we use a principal component analysis based feature extraction technique. The evaluation of the proposed scheme is carried out through standard IEEE 14 bus, 39 bus, 57 bus, and 118 bus systems. The simulation results show that the proposed scheme is proficient at handling non labeled historical measurement datasets and results in a significant improvement in attack detection accuracy.

title: Design and Implementation of an Integrated IoT Blockchain Platform for Sensing Data Integrity
abstract: With the rapid development of communication technologies, the Internet of Things (IoT) is getting out of its infancy, into full maturity, and tends to be developed in an explosively rapid way, with more and more data transmitted and processed. As a result, the ability to manage devices deployed worldwide has been given more and advanced requirements in practical application performances. Most existing IoT platforms are highly centralized architectures, which suffer from various technical limitations, such as a cyber attack and single point of failure. A new solution direction is essential to enhance data accessing, while regulating it with government mandates in privacy and security. In this paper, we propose an integrated IoT platform using blockchain technology to guarantee sensing data integrity. The aim of this platform is to afford the device owner a practical application that provides a comprehensive, immutable log and allows easy access to their devices deployed in different domains. It also provides characteristics of general IoT systems, allows for real time monitoring, and control between the end user and device. The business logic of the application is defined by the smart contract, which contains rules and conditions. The proposed approach is backed by a proof of concept implementation in realistic IoT scenarios, utilizing Raspberry Pi devices and a permissioned network called Hyperledger Fabric. Lastly, a benchmark study using various performance metrics is made to highlight the significance of the proposed work. The analysis results indicate that the designed platform is suitable for the resource constrained IoT architecture and is scalable to be extended in various IoT scenarios.