Conference Papers

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Author: search.filters.author.Chandavarkar, B.R.Subject: search.filters.subject.Trust

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    Performance comparison of executing fast transactions in bitcoin network using verifiable code execution
    (IEEE Computer Society help@computer.org, 2013) Singh, P.; Chandavarkar, B.R.; Arora, S.; Agrawal, N.
    In this paper, we study Bitcoin network for electronic cash transactions, and compare the extension to the BTCs network which inculcates provision of executing fast transactions with greater security and assurance with the former method of Proof-Of-Work for executing transactions. Above milestones are achieved by introducing the concepts of mutual trust and verifiable code execution between the payer and the payee in the network. Our work proposes a significant modification of the Pioneer model to provide a two-party trust framework for Bitcoin transactions; considerably faster compared to the generic trust platform of Bitcoin networks based on slow proof-of-work. The scheme proposed can promote the use of Bitcoin transactions in real life scenarios, where fast transactions are desirable due time constraints between the payment and the service. © 2013 IEEE.
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    Transparency in Content and Source Moderation
    (Springer, 2023) C, A.R.; D, C.S.; D V, P.; Chandavarkar, B.R.
    Content moderation is defined as the process of screening and monitoring user-generated content online. To provide a safe environment for both users and brands, platforms must moderate content to ensure that it falls within pre-established guidelines of acceptable behavior specific to the platform and its audience. Many social media companies employ thousands of employees or volunteers to moderate content manually. These moderators discuss the nature of any questionable posts off-site and remove them if they are deemed inappropriate. Certain platforms also employ automated moderation of content through machine learning models. However, many of them often do not give users any or accurate reasons when their posts are taken down. This lack of transparency in moderation can cause users to believe that their posts were evaluated in a biased manner. To increase users’ trust in the unbiased nature of a platform and still allow for extensive and robust content moderation, we propose a novel algorithm in this chapter. An adaptive machine learning model is used as the initial moderation layer, and then users are allowed to moderate posts through a trust-based social network algorithm. Since machine learning models can gradually improve their performance through feedback and feedback is given in a self-policing fashion, the system enforces both accuracy and transparency for content moderation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Mitigation of Trust-Related Issues in Cryptocurrency Payments Using Machine Learning: A Review
    (Springer Science and Business Media Deutschland GmbH, 2023) Shridhar Kallurkar, H.; Chandavarkar, B.R.
    Cryptocurrency is a type of fiat currency in digital form, unlike the physical money that is commonly used for daily purposes. A blockchain is a base on which a cryptocurrency operates, i.e., it is a growing list of records of transactions happening in a particular cryptocurrency. Trust in a cryptocurrency comes into the picture when two stakeholders, virtually unknown to each other, are confident or not about each other’s reliability in the context of whether each one is getting the service they intended to get. Trust in cryptocurrency can exist between any two stakeholders, such as users, merchants, government agencies, and blockchain technology, who are a part of cryptocurrency transactions. Furthermore, direct or indirect involvement of different stakeholders in cryptocurrency transactions results in issues such as lack of transparency, ease of use, regulations of the government, privacy, security of users, etc. Traditional approaches to anomaly detection in blockchain primarily use machine learning methods because they can infer patterns from historical data to give decent accuracy on test data. This survey presents trust in a cryptocurrency payment and its issues. Furthermore, it also shows the mitigation approaches which use machine learning techniques to address these issues. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.