رشته: مهندسی کامپیوتر

گرایش: امنیت اطلاعات

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The ethical use of big data

For the purpose of this article, ethical use is defined broadly as ensuring that no harm is caused by the use of big data for research. Whilst harm can be caused to many actors in the research process, such as the data source and wider research community, the focus here is on the potential for harm to the data subject. Many organizations and authors have produced ethics criteria for research7 . Whilst these vary depending on the academic discipline and data type, they have commonalities: 1. The project or research should have an element of public good or benefit 2. Data security and confidentiality must be ensured 3. Data subjects should not be identified or harmed as a result of the research 4. The research community should demonstrate trustworthiness 5. Research methodology must be robust and produce statistically valid findings Existing ethics frameworks can demonstrate consideration of these five areas. For example, researchers defining the usefulness or merit that comes from their research (Kassner 2017) and data must be kept secure and results must be robust (Drew, 2016). Metcalf (2014) states that vulnerable people should not be harmed by the research project, and that researchers should demonstrate their trustworthiness so as not to need strict controls. 

The big data world is not idle in this area and much discussion occurs around ethics and ethical data use (Bishop 2017). In the pursuit of an ethical framework, big data could benefit from learning from the social and administrative data world who have several well-established ethical frameworks. More recently the UK Statistics Authority have made considerable investment in the ethical use of secure or legally controlled data. In the quest for consistent, ethical research practice, the UK Statistics Authority have developed a comprehensive framework: a self-assessment tool for researchers to use to conduct a thorough ethics assessment of their research projects. It is a mandatory part of the application process for accessing secure data from the Office for National Statistics but is recommended for all projects that use secondary data sources. It covers 6 main principles, which include 21 items for researchers to assess. The 6 principles cover: 1. The use of data has clear benefits for users and serves the public good 2. The data subject’s identity (whether person or organization) is protected, information is kept confidential and secure, and the issue of consent is considered appropriately 3. The risks and limits of new methods and/or technologies are considered and there is sufficient human oversight so that methods employed are consistent with recognized standards of integrity and quality. 4. Data used and methods employed are consistent with legal requirements such as Data Protection Legislation, the Human Rights Act 1998, the Statistics and Registration Service Act 2007 and the common law duty of confidence 5. The views of the public are considered in light of the data used and the perceived benefits of the research 6. The access, use and sharing of data is transparent, and is communicated clearly and accessibly to the public.

Whilst this framework has been developed with survey and administrative data use in mind, these principles are designed to be embedded into good research practice and Journal Pre-proof 11 would serve as a strong foundation for building a framework for big data research. The remaining article focuses on the first two principles of the UK Statistics Authority ethics framework as these are most directly relevant to discussions around disclosure risk and harm. The discussion will focus on the procedures in UK secure data services and personal experiences from delivering those services. 

(دقت کنید که این بخش از متن، با استفاده از گوگل ترنسلیت ترجمه شده و توسط مترجمین سایت ای ترجمه، ترجمه نشده است و صرفا جهت آشنایی شما با متن میباشد.)