Link: https://ieeexplore.ieee.org/abstract/document/9548968

Abstract:

Many countries across the world have not been entirely successful in their attempts to contain the spread of the COVID-19 virus, which has caused, quite possibly, one of the greatest disasters of the 21st century. A rich variety of contact tracing applications, each offering its own unique set of features, are currently being used to track the spread of the virus from person to person, and isolate/quarantine suspected individuals. However, existing approaches have not been very successful since they lack incentives to motivate users to use them actively. Although each solution provides some form of privacy protection and data security, loopholes do exist, and there is scope for improvement. In this context, we explore the use of blockchain to help in advancing contact tracing applications. In this article, we describe how blockchain could offer enhanced data security and functionality without compromising users' privacy. The effectiveness of contact tracing applications improves with the user base. In this regard, we explore how blockchain-based smart contracts can be used to offer incentives to users at various stages of the contact tracing process. Next, we lay down a plan for future contact tracing frameworks that provide the advantages of blockchain and user incentives, and all the best features available in current solutions.

Link: https://dl.acm.org/doi/abs/10.1145/3474124.3474158

Abstract:

Generally when users share information about themselves on some online platforms, they knowingly or unknowingly allow this data to be used by the companies behind these companies for various purposes including selling this information to advertisers as well as using it to better enrich their predictive models. In the event of the user changing their minds on allowing such data about them to be able to be used by the companies, it becomes a strenuous task for the companies to get rid of the influence of this collected data, especially when it has been used to train their machine learning models. Recent legislations by governing bodies, like the European Union, grant people the right to choose where data about them may be used, including a right to have their data and its resulting influence be completely removed from a company’s databases and machine learning models. To be able to do this at scale new machine unlearning solutions need to be invented. In this paper, we look at some of these early models of machine unlearning strategies that have been proposed.

Link: https://dl.acm.org/doi/abs/10.1145/3549206.3549298

Abstract:

With the rapidly growing numbers of people who have access to a smartphone and an internet connection, the usage of these mobile devices, especially to consume content on social media has also seen a drastic increase. This was accelerated even more due to the pandemic. Such an increased screen time due to social media consumption has left many people feeling less productive, instead focusing on their screens even while at work. A common method people try in order to combat this problem is by deleting or disabling their social media accounts altogether. However, such a tactic more often than not is only a temporary fix as most of the people who try it revert back to their habits in less than a week, or, start feeling a common fear of missing out on their social media updates. In this paper we detail the negative effects of social media addiction, look at suggested methods of combating social media addiction and also propose a method to be able to keep up with the happenings of your social media without feeling the constant need to check their phones repeatedly.