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Register a new userSimilarities and Learnings from Ancient Literature on Blockchain Consensus and Integrity
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Added by
Ashish Kundu
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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In this paper, we have studied how the text of an ancient literature on how their integrity has been preserved for several centuries. Specifically, The Vedas is an ancient literature, which has its text remained preserved without any corruption for thousands of years. As we studied the system that protects the integrity of the text, pronunciation and semantics of the The Vedas, we discovered a number of similarities it has with the current concept of blockchain technology. It is surprising that the notion of de-centralized trust and mathematical encodings have existed since thousands of years in order to protect this work of literature. We have presented our findings and analysis of the similarities. There are also certain technical mechanisms that The Vedic integrity system uses, which can be used to enhance the current digital blockchain platforms in terms of its security and robustness.
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Blockchain technology has drawn attention fromvarious communities. The underlying consensus mechanism inBlockchain enables a myriad of applications for the integrityassurance of stored data. In this paper, we utilize Blockchaintechnology to verify the authenticity of a video captured by astreaming IoT device for forensic investigation purposes. Theproposed approach computes the hash of video frames beforethey leave the IoT device and are transferred to a remote basestation. To guarantee the transmission, we ensure that this hashis sent through a TCP-based connection. The hash is then storedon multiple nodes on a permissioned blockchain platform. Incase the video is modified, the discrepancy will be detected byinvestigating the previously stored hash on the blockchain andcomparing it with the hash of the existing frame in question.In this work, we present the prototype as proof-of-concept withexperiment results. The system has been tested on a RaspberryPi with different quality of videos to evaluate performance. Theresults show that the concept can be implemented with moderatevideo resolutions.
The Internet of Things (IoT) is transforming our physical world into a complex and dynamic system of connected devices on an unprecedented scale. Connecting everyday physical objects is creating new business models, improving processes and reducing costs and risks. Recently, blockchain technology has received a lot of attention from the community as a possible solution to overcome security issues in IoT. However, traditional blockchains (such as the ones used in Bitcoin and Ethereum) are not well suited to the resource-constrained nature of IoT devices and also with the large volume of information that is expected to be generated from typical IoT deployments. To overcome these issues, several researchers have presented lightweight instances of blockchains tailored for IoT. For example, proposing novel data structures based on blocks with decoupled and appendable data. However, these researchers did not discuss how the consensus algorithm would impact their solutions, i.e., the decision of which consensus algorithm would be better suited was left as an open issue. In this paper, we improved an appendable-block blockchain framework to support different consensus algorithms through a modular design. We evaluated the performance of this improved version in different emulated scenarios and studied the impact of varying the number of devices and transactions and employing different consensus algorithms. Even adopting different consensus algorithms, results indicate that the latency to append a new block is less than 161ms (in the more demanding scenario) and the delay for processing a new transaction is less than 7ms, suggesting that our improved version of the appendable-block blockchain is efficient and scalable, and thus well suited for IoT scenarios.
As an emerging technology, blockchain has achieved great success in numerous application scenarios, from intelligent healthcare to smart cities. However, a long-standing bottleneck hindering its further development is the massive resource consumption attributed to the distributed storage and computation methods. This makes blockchain suffer from insufficient performance and poor scalability. Here, we analyze the recent blockchain techniques and demonstrate that the potential of widely-adopted consensus-based scaling is seriously limited, especially in the current era when Moores law-based hardware scaling is about to end. We achieve this by developing an open-source benchmarking tool, called Prism, for investigating the key factors causing low resource efficiency and then discuss various topology and hardware innovations which could help to scale up blockchain. To the best of our knowledge, this is the first in-depth study that explores the next-generation scaling strategies by conducting large-scale and comprehensive benchmarking.
Decentralized finance, i.e., DeFi, has become the most popular type of application on many public blockchains (e.g., Ethereum) in recent years. Compared to the traditional finance, DeFi allows customers to flexibly participate in diverse blockchain financial services (e.g., lending, borrowing, collateralizing, exchanging etc.) via smart contracts at a relatively low cost of trust. However, the open nature of DeFi inevitably introduces a large attack surface, which is a severe threat to the security of participants funds. In this paper, we proposed BLOCKEYE, a real-time attack detection system for DeFi projects on the Ethereum blockchain. Key capabilities provided by BLOCKEYE are twofold: (1) Potentially vulnerable DeFi projects are identified based on an automatic security analysis process, which performs symbolic reasoning on the data flow of important service states, e.g., asset price, and checks whether they can be externally manipulated. (2) Then, a transaction monitor is installed offchain for a vulnerable DeFi project. Transactions sent not only to that project but other associated projects as well are collected for further security analysis. A potential attack is flagged if a violation is detected on a critical invariant configured in BLOCKEYE, e.g., Benefit is achieved within a very short time and way much bigger than the cost. We applied BLOCKEYE in several popular DeFi projects and managed to discover potential security attacks that are unreported before. A video of BLOCKEYE is available at https://youtu.be/7DjsWBLdlQU.
Mixed reality (MR) technology development is now gaining momentum due to advances in computer vision, sensor fusion, and realistic display technologies. With most of the research and development focused on delivering the promise of MR, there is only barely a few working on the privacy and security implications of this technology. This survey paper aims to put in to light these risks, and to look into the latest security and privacy work on MR. Specifically, we list and review the different protection approaches that have been proposed to ensure user and data security and privacy in MR. We extend the scope to include work on related technologies such as augmented reality (AR), virtual reality (VR), and human-computer interaction (HCI) as crucial components, if not the origins, of MR, as well as numerous related work from the larger area of mobile devices, wearables, and Internet-of-Things (IoT). We highlight the lack of investigation, implementation, and evaluation of data protection approaches in MR. Further challenges and directions on MR security and privacy are also discussed.
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