@conference {570, title = {Making Blockchain Validators Honest}, booktitle = {2022 Fourth International Conference on Blockchain Computing and Applications (BCCA)}, year = {2022}, abstract = {

The importance of honesty among blockchain validators can not be overemphasized, especially as blockchain is used by many as an underlying technology for the development of various Industry 4.0 solutions. In the blockchain consensus process, validators validate the correctness of transactions and propose new blocks for addition to the blockchain. They are rewarded for this task with the blockchain native token (e.g., ETH on the Ethereum blockchain). This reward is often distributed among validators with respect to their staked amount. An increasing number of validators joining the network leads to a decreasing chance of a validator being chosen for the validation task and thus a reduction in the validation reward. This situation results in some form of competition among the validators, leading them to carry out various malicious actions to influence the blockchain validator selection protocol in order to be chosen. In this paper, we examine the competitive interactions between validators in a blockchain consensus process and propose a model using an infinitely repeated game model that ensures that the validators are deterred from behaving maliciously while also encouraging a self-policing notion due to the extra incentive mechanism of an improved reputation score when a validator can verifiably report malicious activities by others. Further, we discuss the factors that can incentivize or disincentivize a validator to either continue to behave honestly or switch to dishonest behavior.

}, doi = {10.1109/BCCA55292.2022.9921952}, url = {https://ieeexplore.ieee.org/abstract/document/9921952}, author = {Salau, Abiola and Dantu, Ram and Morozov, Kirill and Badruddoja, Syed and Upadhyay, Kritagya} } @conference {575, title = {Making Smart Contracts Predict and Scale}, booktitle = {2022 Fourth International Conference on Blockchain Computing and Applications (BCCA)}, year = {2022}, abstract = {

The machine learning algorithms can predict the events based on the trained models and datasets. However, a reliable prediction requires the model to be trusted and tamper-resistant. Blockchain technology provides trusted output with consensus-based transactions and an immutable distributed ledger. The machine learning algorithms can be trained on blockchain smart contracts to produce trusted models for reliable prediction. But most smart contracts in the blockchain do not support floating-point data type, limiting computations for classification, which can affect the prediction accuracy. In this work, we propose a novel method to produce floating-point equivalent probability estimation to classify labels on-chain with a Naive Bayes algorithm. We derive a mathematical model with Taylor series expansion to compute the ratio of the posterior probability of classes to classify labels using integers. Moreover, we implemented our solution in Ethereum blockchain smart-contract with the Solidity programming language, where we achieved a prediction accuracy comparable to the scikit-learn library in Python. Our derived method is platform-agnostic and can be supported in any blockchain network. Furthermore, machine learning and deep-learning algorithms can borrow the derived method.

}, doi = {10.1109/BCCA55292.2022.9922480}, author = {Badruddoja, Syed and Dantu, Ram and He, Yanyan and Thompson, Mark and Salau, Abiola and Upadhyay, Kritagya} } @conference {572, title = {Smarter Contracts to Predict using Deep-Learning Algorithms}, booktitle = {2022 Fourth International Conference on Blockchain Computing and Applications (BCCA)}, year = {2022}, abstract = {

Deep learning techniques can predict cognitive intelligence from large datasets involving complex computations with activation functions. However, the prediction output needs verification for trust and reliability. Moreover, these algorithms suffer from the model{\textquoteright}s provenance to keep track of model updates and developments. Blockchain smart contracts provide a trustable ledger with consensus-based decisions that assure integrity and verifiability. In addition, the immutability feature of blockchain also supports the provenance of data that can help deep learning algorithms. Nevertheless, smart contract languages cannot predict due to the absence of floating-point operations required by activation functions of neural networks. In this paper, we derive a novel method using the Taylor series expansion to compute the floating-point equivalent output for activation functions. We train the deep learning model off-chain using a standard Python programming language. Moreover, we store models and predict on-chain with blockchain smart contracts to produce a trusted forecast. Our experiment and analysis achieved an accuracy (99\%) similar to popular Keras Python library models for the MNIST dataset. Furthermore, any blockchain platform can reproduce the activation function using our derived method. Last but not least, other deep learning algorithms can reuse the mathematical model to predict on-chain.

}, doi = {10.1109/BCCA55292.2022.9922240}, author = {Badruddoja, Syed and Dantu, Ram and He, Yanyan and Thompson, Mark and Salau, Abiola and Upadhyay, Kritagya} } @conference {503, title = {Towards a Threat Model and Security Analysis for Data Cooperatives}, booktitle = {19th International Conference on Security and Cryptography (SECRYPT 2022)}, year = {2022}, abstract = {

Data cooperative (called {\textquotedblleft}data coop{\textquotedblright} for short) is an emerging approach in the area of secure data management. It promises its users a better protection and control of their data, as compared to the traditional way of their handling by the data collectors (such as governments, big data companies, and others). However, for the success of data coops, existing challenges with respect to data management systems need to be adequately addressed. Especially, they concern terms of security and privacy, as well as the power imbalance between providers/owners and collectors of data. Designing a security and privacy model for a data coop requires a systematic threat modeling approach that identifies the security landscape, attack vectors, threats, and vulnerabilities, as well as the respective mitigation strategies. In this paper, we analyze the security of data cooperatives, identify potential security risks and threats, and suggest adequate countermeasures. We also discuss existing challenges that hinder the widespread adoption of data coops.\ 

}, keywords = {Cybersecurity, Data Cooperatives, Secure Data Management, Security and Privacy., Threat Model}, doi = {10.5220/0011328700003283}, author = {Salau, Abiola and Dantu, Ram and Morozov, Kirill and Upadhyay, Kritagya and Badruddoja, Syed} } @conference {565, title = {Trusted AI with Blockchain to Empower Metaverse}, booktitle = {2022 Fourth International Conference on Blockchain Computing and Applications (BCCA)}, year = {2022}, abstract = {

The digital experience emerging in the virtual world is a reality with the advent of the metaverse. Augmented reality(AR), virtual reality(VR), extended reality(XR), and artificial intelligence(AI) algorithms would pave the way for an immersive experience for the users in the virtual space. However, the explosion of these technologies broaches new challenges to threaten the success of metaverse due to security risks. The blockchain technology augmented with AI promises to deliver a trusted metaverse for everyone. Nevertheless, smart contracts fail to produce a cognitive prediction, dissuading users from confiding in the metaverse. We arm smart contracts with intelligence to predict using AI algorithms. Moreover, we deploy the smart contracts on the Ethereum blockchain platform and produce a prediction accuracy of 95\% compared to Python scikit-learn-based predictions. Our results show that the prediction delay can obstruct the growth of metaverse applications to accept blockchain technologies. Furthermore, the limitation of blockchain technology can make integration unreasonable. Therefore, we discuss possible scalability solutions that can be part of our future work to help more metaverse applications adopt blockchain solutions.

}, doi = {10.1109/BCCA55292.2022.9922027}, author = {Badruddoja, Syed and Dantu, Ram and He, Yanyan and Thompson, Mark and Salau, Abiola and Upadhyay, Kritagya} } @conference {522, title = {Can{\textquoteright}t Understand SLAs? Use the Smart Contract}, booktitle = {2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Atlanta, GA, USA}, abstract = {

A Service Level Agreement (SLA) is a special kind of legal contract that binds a vendor to its customers where the vendor commits to provide certain services in exchange for certain payment from the customers. On the other hand, a Smart Contract is a contract that is a computer program that also binds multiple parties into given agreements but is a set of a precise rules and is self-enforceable and self-executable. Since almost all legal contracts are ambiguous by nature and are complex to read and understand, we perform a novel study on how we can replace the traditional vague legal contract with the smart contact and the effect of the ambiguity on the smart contract by performing a thorough analysis on SLAs by measuring their ambiguities in various aspects. We take several samples of real SLAs from six different popular broadband vendors. We use four random SLAs to train the machine learning model to classify and then detect ambiguous words in two unseen SLAs which were the SLAs of Ziply Fiber and Century Link. As different people form different interpretations while reading the ambiguous legal contracts, we generate various human interpretations from the machine detected ambiguous words and convert all those generated interpretations into Smart Contracts to perform testing in Ethereum-based Blockchain to identify the most ambiguous as well as accurate interpretation of the SLA. From our analysis and observation, we were able to find out the most ambiguous interpretation of SLAs and we concluded that the SLA of Ziply Fiber was more ambiguous in general compared to the SLA of Century Link. Moreover, our proposed approach to detect ambiguous terms and to translate an ambiguous legal contract to a smart legal contract using a formal language to measure the degree of ambiguity can be extrapolated and replicated to legal contracts from other types of industries as well.

}, keywords = {ambiguity, ambiguity index, blockchain, clauses, complexity, Ethereum, interpretations, machine learning, service level agreement, SLA, smart contract, smart legal contract}, isbn = {978-1-6654-1623-8}, doi = {10.1109/TPSISA52974.2021.00015}, url = {https://ieeexplore.ieee.org/abstract/document/9750250}, author = {Upadhyay, Kritagya and Dantu, Ram and He, Yanyan and Badruddoja, Syed and Salau, Abiola} } @conference {528, title = {Data Cooperatives for Neighborhood Watch}, booktitle = {2021 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Sydney, Australia}, abstract = {

The increasing proliferation of user data is moving the world from the era of {\textquoteright}big data{\textquoteright} to a new era of shared data, and some are considering data as a factor of production that is paving the way for a new business economy. In this paper, we propose a solution that uses blockchain technology as a platform for online neighborhood watch using a form of data cooperative among individuals or organizations in the sharing of data through a peer-to-peer mechanism. We prove the concept by implementing a distributed phishing data sharing system that will maintain a community ledger of reported phishing activities with a consensus-based approval of the phishing transaction and a novel reputation scoring system thereby adding reliability to the system and effectively tackling the phishing problem. The data cooperative provides a way for timely multi-party sharing of phishing data among anti-phishing organizations and users of the internet eliminating the current approach of each organization maintaining its database. Our results show that blockchain is effective in complementing the existing methods of phishing detection and serves as a platform for sharing phishing data with respect to scalability, cost, and memory consumption. Also, our results further show that transaction times on the Ropsten test net follow a Gamma distribution. Our approach can be extrapolated to other data sharing systems like medical data, spam calls, discussion forums, etc.

}, keywords = {blockchain, Cybersecurity, data cooperative, data sharing, distributed ledger, peer-to- peer, phishing, smart contract}, isbn = {978-1-6654-3578-9}, doi = {10.1109/ICBC51069.2021.9461056}, url = {https://ieeexplore.ieee.org/abstract/document/9461056}, author = {Salau, Abiola and Dantu, Ram and Upadhyay, Kritagya} } @conference {513, title = {Make Consumers Happy by Defuzzifying the Service Level Agreements}, booktitle = {2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Atlanta, GA, USA}, abstract = {

A Service Level Agreement (SLA) is a special kind of legal contract that binds a vendor to its customers where the vendor commits to provide certain services in exchange for certain payments from the customers. However, when customers do not get the services that they have subscribed for, it becomes a laborious job for customers to contact or visit the company and claim the correct amount of compensation or service credits. On the other hand, a Smart Contract is a contract that is a computer program that also binds multiple parties into given agreements but is a set of precise rules and is self-enforceable and self-executable. In this paper, we have introduced a novel work where we use fuzzy logic inside the Ethereum-based smart contract for two significant objectives. The first objective is to make the claim of the compensation easier and faster for customers by translating the SLA into a smart contract. The second objective is to make the smart contract even smarter and intelligent by implementing fuzzy logic so that customers who have a hard time understanding the legal jargon and ambiguities of the legal contract and SLA to find out if the compensation amount they are getting when the service is poor is good enough. Since fuzzy logic models semantics of linguistic expressions by capturing vagueness in the fuzzy sets, it becomes easier to solve the problem of contractual ambiguities and expedite the process of claiming compensation when implemented in a Blockchain-based smart contract.

}, keywords = {ambiguity, blockchain, clauses, complexity, Ethereum, Fuzzy logic, interpretations, service level agreement, SLA, smart contract, smart legal contract}, isbn = {978-1-6654-1623-8}, doi = {10.1109/TPSISA52974.2021.00011}, url = {https://ieeexplore.ieee.org/abstract/document/9750258}, author = {Upadhyay, Kritagya and Dantu, Ram and He, Yanyan and Salau, Abiola and Badruddoja, Syed} } @conference {518, title = {Paradigm Shift from Paper Contracts to Smart Contracts}, booktitle = {2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Atlanta, GA, USA}, abstract = {

The ambiguity and complexity of the traditional legal contracts have motivated the study and exploration of a better and advanced contract known as blockchain-based smart contracts. A smart contract is a self-executable contract where the terms of the agreement between the involved parties are directly written into the lines of code that resides in the distributed ledger technology known as the blockchain. Obtaining a better understanding of smart contracts to overcome the fundamental issues of traditional legal contracts is vital for the successful and faster dispute settlement process without the intervention of any third-party mediators like courts, banks, lawyers, etc. In this paper, we present a comprehensive overview of the key features of the paradigm shift from traditional paper contracts to smart contracts. In addition, we also discuss why smart contracts are necessary to be legally enforced and the crucial conditions that are required for them to be legally enforceable. Furthermore, we outline recent trends and emerging technologies such as Natural Language Processing, Machine Learning, and the Internet of Things that have been combined together with smart contracts.

}, keywords = {ambiguity, artificial intelligence, blockchain, electronic contract, enforceability, Ethereum, interpretations, smart contract, traditional legal contract}, isbn = {978-1-6654-1623-8}, doi = {10.1109/TPSISA52974.2021.00029}, url = {https://ieeexplore.ieee.org/abstract/document/9750226}, author = {Upadhyay, Kritagya and Dantu, Ram and He, Yanyan and Salau, Abiola and Badruddoja, Syed} }