A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle
Over the decades, Artificial Intelligence (AI) and machine learning has become a transformative solution in many sectors, services, and technology platforms in a wide range of applications, such as in smart healthcare, financial, political, and surveillance systems. In such applications, a large amo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10155147/ |
| _version_ | 1797796504244584448 |
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| author | Sakib Shahriar Sonal Allana Seyed Mehdi Hazratifard Rozita Dara |
| author_facet | Sakib Shahriar Sonal Allana Seyed Mehdi Hazratifard Rozita Dara |
| author_sort | Sakib Shahriar |
| collection | DOAJ |
| description | Over the decades, Artificial Intelligence (AI) and machine learning has become a transformative solution in many sectors, services, and technology platforms in a wide range of applications, such as in smart healthcare, financial, political, and surveillance systems. In such applications, a large amount of data is generated about diverse aspects of our life. Although utilizing AI in real-world applications provides numerous opportunities for societies and industries, it raises concerns regarding data privacy. Data used in an AI system are cleaned, integrated, and processed throughout the AI life cycle. Each of these stages can introduce unique threats to individual’s privacy and have an impact on ethical processing and protection of data. In this paper, we examine privacy risks in different phases of the AI life cycle and review the existing privacy-enhancing solutions. We introduce four different categories of privacy risk, including (i) risk of identification, (ii) risk of making an inaccurate decision, (iii) risk of non-transparency in AI systems, and (iv) risk of non-compliance with privacy regulations and best practices. We then examined the potential privacy risks in each AI life cycle phase, evaluated concerns, and reviewed privacy-enhancing technologies, requirements, and process solutions to countermeasure these risks. We also reviewed some of the existing privacy protection policies and the need for compliance with available privacy regulations in AI-based systems. The main contribution of this survey is examining privacy challenges and solutions, including technology, process, and privacy legislation in the entire AI life cycle. In each phase of the AI life cycle, open challenges have been identified. |
| first_indexed | 2024-03-13T03:34:05Z |
| format | Article |
| id | doaj.art-1461daf0385e4eb68b7f5aec05ffc081 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T03:34:05Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1461daf0385e4eb68b7f5aec05ffc0812023-06-23T23:00:38ZengIEEEIEEE Access2169-35362023-01-0111618296185410.1109/ACCESS.2023.328719510155147A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life CycleSakib Shahriar0Sonal Allana1https://orcid.org/0000-0002-4879-276XSeyed Mehdi Hazratifard2Rozita Dara3https://orcid.org/0000-0002-3728-0275School of Computer Science, University of Guelph, Guelph, CanadaSchool of Computer Science, University of Guelph, Guelph, CanadaDepartment of Electrical and Computer Engineering, University of Victoria, Victoria, CanadaSchool of Computer Science, University of Guelph, Guelph, CanadaOver the decades, Artificial Intelligence (AI) and machine learning has become a transformative solution in many sectors, services, and technology platforms in a wide range of applications, such as in smart healthcare, financial, political, and surveillance systems. In such applications, a large amount of data is generated about diverse aspects of our life. Although utilizing AI in real-world applications provides numerous opportunities for societies and industries, it raises concerns regarding data privacy. Data used in an AI system are cleaned, integrated, and processed throughout the AI life cycle. Each of these stages can introduce unique threats to individual’s privacy and have an impact on ethical processing and protection of data. In this paper, we examine privacy risks in different phases of the AI life cycle and review the existing privacy-enhancing solutions. We introduce four different categories of privacy risk, including (i) risk of identification, (ii) risk of making an inaccurate decision, (iii) risk of non-transparency in AI systems, and (iv) risk of non-compliance with privacy regulations and best practices. We then examined the potential privacy risks in each AI life cycle phase, evaluated concerns, and reviewed privacy-enhancing technologies, requirements, and process solutions to countermeasure these risks. We also reviewed some of the existing privacy protection policies and the need for compliance with available privacy regulations in AI-based systems. The main contribution of this survey is examining privacy challenges and solutions, including technology, process, and privacy legislation in the entire AI life cycle. In each phase of the AI life cycle, open challenges have been identified.https://ieeexplore.ieee.org/document/10155147/Artificial intelligencemachine learningAI life cycleprivacy riskprivacy legislationprivacy enhancing solutions |
| spellingShingle | Sakib Shahriar Sonal Allana Seyed Mehdi Hazratifard Rozita Dara A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle IEEE Access Artificial intelligence machine learning AI life cycle privacy risk privacy legislation privacy enhancing solutions |
| title | A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle |
| title_full | A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle |
| title_fullStr | A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle |
| title_full_unstemmed | A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle |
| title_short | A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle |
| title_sort | survey of privacy risks and mitigation strategies in the artificial intelligence life cycle |
| topic | Artificial intelligence machine learning AI life cycle privacy risk privacy legislation privacy enhancing solutions |
| url | https://ieeexplore.ieee.org/document/10155147/ |
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