Privacy Enhancing Computation
In the 21st century, data is a big deal that manages everything in society. In the use of data, it is essential to be secured the privacy of data because data create various types of risk in society as it is susceptible in various ways. Any single failure of ensuring trust in data may lead to a colossal disaster that cost financially and politically, and in many ways. So in this process, a range of technologies where Privacy Enhancing Computation is actively involved is used to ensure a trusted environment for data sharing, decentralized analytics, and encryption of data in use.
There are a few common reasons to implement Privacy-enhancing Computation(PEC). In some cases, we face failure to protect will lead to easy access to information by someone who wants to own it without permission; also, misrepresentation is one reason where the information present by changing its original meaning. Not only that, but there are unfair conditions also a reason to implement PEC. where people cannot track what gets done with their data when they fully trust third-party providers.
One of the big four accounting firm, Delloite, release a report in 2019 regarding what privacy-enhancing techniques used in the financial sector. This report mentioned five essential techniques ; Differential Privacy, Federated Analysis, Homomorphic Encryption, Zero-Knowledge Proof, Secure Multi-Party Computation. As a great sign, Deloitte is working with the World Economic Forum to evaluate the forces of change in financial services.
Then we have to discuss what sort of protection these privacy-enhancing techniques(PETs) will provide in the current scenario. As per the literature work “Protecting privacy in practice” published by the Royal Society, Different PETs may accomplish various objectives , including Providing private data set access safely, allowing different organizations' private data to be analyzed jointly, Outsourcing private data computations to the cloud in a secure manner and Services that depend on user data should be decentralized. At the moment, there is no single technology to deal with every scenario at once. Nevertheless, in future, there may be a single framework implemented to deal with any case.
The United Kingdom, the European Union, and the United States fund and directed many research projects to improve PEC as its acute problem with rising technology and Big Data usage. We need to give attention to adopt this concept in different scenarios. In one case, we face a considerable challenge to make awareness about this concept in the world. We have to add this concept to standards for quality assurance. Also, we have to invest more human resources by implementing this in postgraduate studies and attract scholars to promote an innovation ecosystem of PEC.
Still, we are not at the peak of using data. Every second, the use of data is increasing tremendously. So the importance of PEC is to keep the increase. As this is very sensitive to everyone's lives, we need to talk about this to keep awareness in our society; also, we need to involve developing innovative ecosystems to promote PEC actively.
Bibliography
[1] Das, P., 2020. Data in Use, Data Sharing and Privacy Enhancing Computation. [Online]
Available at: https://www.soterosoft.com/data-in-use-data-sharing.../
[Accessed 27 02 2021].
[2] Deloitte Touche Tohmatsu Limited, 2019. Data Sharing in Financial Services: Five Techniques to enhance privacy and confidentiality, s.l.: Deloitte Touche Tohmatsu Limited.
[3] Mikula, A., 2020. Privacy-Enhancing Computation: Data Protection Technologies. [Online]
Available at: https://gbksoft.com/blog/privacy-enhancing-computation/
[Accessed 27 02 2021].
[4] The Royal Society, 2019. Protecting privacy in practice: The current use,development and limits of Privacy Enhancing Technologies in data analysis, London: The Royal Society.
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