A lightweight blockchain based framework for underwater ioT

The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Integrity Proofs for RDF Graphs

Representing open datasets with the RDF model is becoming increasingly popular. An important aspect of this data model is that it can utilize the methods of computing cryptographic hashes to verify the integrity of RDF graphs. In this paper, we first develop a number of metrics to compare the state-of-the-art integrity proof methods and then present two new approaches to generate an integrity proof of RDF datasets: (i) semantic-based and (ii) structure-based. The semantic-based approach leverages timestamps (or other inherent notions of ordering) as an indexing key to construct a sorted Merkle tree variation, where timestamps are semantically extractable from the dataset. The structure-based approach utilizes the redundant structure of large RDF datasets to compress the dataset statements prior to generating a variation of a Merkle tree. We provide a theoretical analysis and an experimental evaluation of our two proposed methods. Compared to the Merkle and sorted Merkle tree, the semantic-based approach achieves faster querying performance for large datasets. The structure-based approach is well suited when RDF datasets contain large amounts of semantic redundancies. We also evaluate our methods' resistance to adversarial threats

Integrity and Privacy Protection for Cyber-physical Systems (CPS)

The present-day interoperable and interconnected cyber-physical systems (CPS) provides significant value in our daily lives with the incorporation of advanced technologies. Still, it also increases the exposure to many security privacy risks like (1) maliciously manipulating the CPS data and sensors to compromise the integrity of the system (2) launching internal/external cyber-physical attacks on the central controller dependent CPS systems to cause a single point of failure issues (3) running malicious data and query analytics on the CPS data to identify internal insights and use it for achieving financial incentive. Moreover, (CPS) data privacy protection during sharing, aggregating, and publishing has also become challenging nowadays because most of the existing CPS security and privacy solutions have drawbacks, like (a) lack of a proper vulnerability characterization model to accurately identify where privacy is needed, (b) ignoring data providers privacy preference, (c) using uniform privacy protection which may create inadequate privacy for some provider while overprotecting others.Therefore, to address these issues, the primary purpose of this thesis is to orchestrate the development of a decentralized, p2p connected data privacy preservation model to improve the CPS system's integrity against malicious attacks. In that regard, we adopt blockchain to facilitate a decentralized and highly secured system model for CPS with self-defensive capabilities. This proposed model will mitigate data manipulation attacks from malicious entities by introducing bloom filter-based fast CPS device identity validation and Merkle tree-based fast data verification. Finally, the blockchain consensus will help to keep consistency and eliminate malicious entities from the protection framework. Furthermore, to address the data privacy issues in CPS, we propose a personalized data privacy model by introducing a standard vulnerability profiling library (SVPL) to characterize and quantify the CPS vulnerabilities and identify the necessary privacy requirements. Based on this model, we present our personalized privacy framework (PDP) in which Laplace noise is added based on the individual node's selected privacy preferences. Finally, combining these two proposed methods, we demonstrate that the blockchain-based system model is scalable and fast enough for CPS data's integrity verification. Also, the proposed PDP model can attain better data privacy by eliminating the trade-off between privacy, utility, and risk of losing information

HEX-BLOOM: An Efficient Method for Authenticity and Integrity Verification in Privacy-preserving Computing

Merkle tree is applied in diverse applications, namely, Blockchain, smart grid, IoT, Biomedical, financial transactions, etc., to verify authenticity and integrity. Also, the Merkle tree is used in privacy-preserving computing. However, the Merkle tree is a computationally costly data structure. It uses cryptographic string hash functions to partially verify the data integrity and authenticity of a data block. However, the verification process creates unnecessary network traffic because it requires partial hash values to verify a particular block. Moreover, the performance of the Merkle tree also depends on the network latency. Therefore, it is not feasible for most of the applications. To address the above issue, we proposed an alternative model to replace the Merkle tree, called HEX-BLOOM, and it is implemented using hash, Exclusive-OR and Bloom Filter. Our proposed model does not depends on network latency for verification of data block\u27s authenticity and integrity. HEX-BLOOM uses an approximation model, Bloom Filter. Moreover, it employs a deterministic model for final verification of the correctness. In this article, we show that our proposed model outperforms the state-of-the-art Merkle tree in every aspect

Data query mechanism based on hash computing power of blockchain in internet of things

Funding: This work is supported by the NSFC (61772280, 61772454, 61811530332, 61811540410), the PAPD fund from NUIST. This work was funded by the Researchers Supporting Project No. (RSP-2019/102) King Saud University, Riyadh, Saudi Arabia. Jin Wang and Osama Alfarraj are the corresponding authors. Acknowledgments: We thank Researchers Supporting Project No. (RSP-2019/102) King Saud University, Riyadh, Saudi Arabia for funding this paper. Author Contributions: Y.R., F.Z. and O.A. conceived the mechanism design and wrote the paper, P.K.S. built the models. T.W. and A.T. developed the mechanism, J.W. and O.A. revised the manuscript. All authors have read and agreed to the published version of the manuscript.Peer reviewedPublisher PD

Enhancing Data Integrity in Blockchain through Fuzzy Augmented Lagrangian Optimization and Compact Blocks to Minimize Redundancy

Blockchain is a method of storing data that makes it difficult or impossible to modify, steal, or swindle the system. Every block in a blockchain has its header with the unique nonce, timestamp, hash, the previous hash, transaction data, and the Merkle root. The Merkle tree is crucial in a block for consolidating data into a single hash, but it can suffer from data redundancy concerns during its structure formation. The central focus of the paper revolves around data redundancy and presents a novel approach for ensuring data integrity in blockchain with a compactness technique. Compactness is accomplished using Fuzzy Augmented Lagrangian Optimization to reduce data redundancy (FALORR). We integrate compact blocks into regular blockchain setup, bringing out a faster and more efficient way to reduce memory requirements. This effectual transaction verification structure improves the overall security and efficiency of the blockchain network by detecting and preventing malicious activities. To evaluate the effectiveness of the proposed system, we employed Hyperledger Caliper, a specialized benchmarking tool tailored for gauging the performance of blockchain solutions. The results of our implementation and evaluation demonstrate the effectiveness of the proposed structure in minimizing data redundancy and maintaining the data integrity of transactions in the blockchain system

Ensuring the Data Integrity and Confidentiality in Cloud Storage Using Hash Function and TPA

Main call for Cloud computing is that users only utilize what they required and only pay for whatever they are using. Mobile Cloud Computing refers to an infrastructure where data processing and storage can happen away from mobile device. Research estimates that mobile subscribers worldwide will reach 15 billion by the end of 2014 and 18 billion by at the ending of 2016. Due to increasing use of mobile devices the requirement of cloud computing in mobile devices arise, which evolves Mobile Cloud Computing. Mobile devices require large storage capacity and maximum CPU speed. As we are storing data on cloud there is an issue of data security. As there is risk associated with data storage many IT professionals are not showing their interest towards Mobile Cloud Computing. To ensure the users' data correctness in the cloud, here we are proposing an effective mechanism with salient feature of data integrity and confidentiality. This paper proposed a solution which uses the RSA algorithm and mechanism of hash function along with various cryptography tools to provide better security to the data stored on the cloud. This model can not only solve the problem of storage of massive data, but also make sure that it will give data access control mechanisms and ensure sharing data files with confidentiality and integrity. DOI: 10.17762/ijritcc2321-8169.15055

Improving Data Availability in Decentralized Storage Systems

PhD thesis in Information technologyPreserving knowledge for future generations has been a primary concern for humanity since the dawn of civilization. State-of-the-art methods have included stone carvings, papyrus scrolls, and paper books. With each advance in technology, it has become easier to record knowledge. In the current digital age, humanity may preserve enormous amounts of knowledge on hard drives with the click of a button. The aggregation of several hard drives into a computer forms the basis for a storage system. Traditionally, large storage systems have comprised many distinct computers operated by a single administrative entity. With the rise in popularity of blockchain and cryptocurrencies, a new type of storage system has emerged. This new type of storage system is fully decentralized and comprises a network of untrusted peers cooperating to act as a single storage system. During upload, files are split into chunks and distributed across a network of peers. These storage systems encode files using Merkle trees, a hierarchical data structure that provides integrity verification and lookup services. While decentralized storage systems are popular and have a user base in the millions, many technical aspects are still in their infancy. As such, they have yet to prove themselves viable alternatives to traditional centralized storage systems. In this thesis, we contribute to the technical aspects of decentralized storage systems by proposing novel techniques and protocols. We make significant contributions with the design of three practical protocols that each improve data availability in different ways. Our first contribution is Snarl and entangled Merkle trees. Entangled Merkle trees are resilient data structures that decrease the impact hierarchical dependencies have on data availability. Whenever a chunk loss is detected, Snarl uses the entangled Merkle trees to find parity chunks to repair the lost chunk. Our results show that by encoding data as an entangled Merkle tree and using Snarl’s repair algorithm, the storage utilization in current systems could be improved by over five times, with improved data availability. Second, we propose SNIPS, a protocol that efficiently synchronizes the data stored on peers to ensure that all peers have the same data. We designed a Proof of Storage-like construction using a Minimal Perfect Hash Function. Each peer uses the PoS-like construction to create a storage proof for those chunks it wants to synchronize. Peers exchange storage proofs and use them to efficiently determine which chunks they are missing. The evaluation shows that by using SNIPS, the amount of synchronization data can be reduced by three orders of magnitude in current systems. Lastly, in our third contribution, we propose SUP, a protocol that uses cryptographic proofs to check if a chunk is already stored in the network before doing wasteful uploads. We show that SUP may reduce the amount of data transferred by up to 94 % in current systems. The protocols may be deployed independently or in combination to create a decentralized storage system that is more robust to major outages. Each of the protocols has been implemented and evaluated on a large cluster of 1,000 peers