Privacy-Preserving Machine Learning

This book provides an overview of the evolution of privacy-preserving machine learning schemes and proposes solutions to address privacy issues in machine learning tasks involving multiple parties.

Format: Paperback / softback
Length: 88 pages
Publication date: 15 March 2022
Publisher: Springer Verlag, Singapore

This comprehensive book delves into the intricate evolution of privacy-preserving machine learning schemes over the past decade, shedding light on the critical importance of privacy-preserving techniques in the ever-evolving landscape of the Internet. In response to the diverse range of Internet services, data services powered by machine learning have emerged, offering a wide array of applications, from risk assessment to image recognition. However, the proliferation of machine learning-based applications in open access environments and the inherent lack of trust pose significant security and privacy risks. Recognizing these challenges, the book presents a series of in-depth studies aimed at addressing privacy concerns in machine learning tasks involving multiple parties. Furthermore, it offers a comprehensive exploration of state-of-the-art privacy-preserving techniques, highlighting their strengths and weaknesses. The book concludes by examining the emerging security threats faced by these techniques, underscoring the urgent need for continued research and development in the field. This invaluable resource provides researchers, practitioners, and policymakers with a comprehensive understanding of the past, present, and future of privacy-preserving machine learning, enabling them to navigate the complex terrain of data privacy in the digital age.

Introduction:
Privacy has become an increasingly important concern in the digital age, as the vast amounts of personal data collected and processed daily pose significant risks to individuals' privacy and security. Machine learning, a powerful tool for analyzing and extracting insights from data, has the potential to revolutionize various industries, including healthcare, finance, and transportation. However, the use of machine learning also raises concerns about privacy, as it involves the processing and analysis of sensitive personal information.
Evolution of Privacy-Preserving Machine Learning Schemes:
Over the past decade, there has been a significant effort to develop privacy-preserving machine learning schemes that can protect individuals' privacy while enabling the use of machine learning for data analysis. This evolution has been driven by the need to address the diverse range of privacy concerns that arise in different applications.
Importance of Privacy-Preserving Techniques:
Privacy-preserving techniques are essential for ensuring that individuals' privacy is protected while using machine learning-based applications. These techniques include data anonymization, k-anonymization, and differential privacy, which allow for the processing of data without disclosing personal information.
Applications of Machine Learning in Data Services:
Machine learning-based data services are now available for a wide range of applications, including risk assessment, image recognition, and natural language processing. These services rely on the processing and analysis of large datasets, which often contain sensitive personal information.
Security and Privacy Risks:
Despite the benefits of machine learning, there are significant security and privacy risks associated with its use. These risks include data breaches, unauthorized access, and misuse of personal information. Machine learning-based applications also face the challenge of balancing privacy protection with the need for data analysis and insights.
Addressing Privacy Issues:
To address privacy issues in machine learning, researchers have developed a range of solutions. These solutions include privacy-enhancing technologies, such as encryption and tokenization, as well as privacy-preserving algorithms, such as federated learning and secure multi-party computation.
Proposed Solutions:
In addition to these technological solutions, there are also proposed policy and regulatory frameworks that can help ensure privacy protection in machine learning. These frameworks include data protection laws, privacy-by-design principles, and transparency requirements for data processing.
State-of-the-Art Privacy-Preserving Techniques:
State-of-the-art privacy-preserving techniques include data anonymization, k-anonymization, and differential privacy. These techniques allow for the processing of data without disclosing personal information, while still enabling the use of machine learning for data analysis.
Conclusion:
In conclusion, the evolution of privacy-preserving machine learning schemes over the past decade has been driven by the need to address the diverse range of privacy concerns that arise in different applications. While there are significant security and privacy risks associated with machine learning, there are also a range of solutions and proposed frameworks that can help ensure privacy protection. As the use of machine learning continues to grow, it is essential that researchers, practitioners, and policymakers continue to work together to develop and implement effective privacy-preserving techniques.

Weight: 163g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811691386
Edition number: 1st ed. 2022