מעודכן ל: 11.7.2024
Artificial Intelligence Security and Privacy by
This two-volume set LNCS 14509-14510, constitutes the refereed proceedings of the First International Conference on Artificial Intelligence Security and Privacy, AIS&P 2023, held in Guangzhou, China, during December 3-5, 2023. The 40 regular papers and 23 workshop papers presented in this two-volume set were carefully reviewed and selected from 115 submissions.Topics of interest include, e.g., attacks and defence on AI systems; adversarial learning; privacy-preserving data mining; differential privacy; trustworthy AI; AI fairness; AI interpretability; cryptography for AI; security applications.
The Law and Economics of Privacy, Personal Data, Artificial Intelligence, and Incomplete Monitoring by
The Law and Economics of Privacy, Personal Data, Artificial Intelligence, and Incomplete Monitoringpresents new findings and perspectives from leading international scholars on several emerging areas issues in legal and economic research. The collection contains new theoretical papers on privacy, the protection of personal data, the use of regulatory monitoring under legal standards versus rules, a study of the properties of market efficiency in securities fraud litigation, as well as an analysis of non-exclusionary price floors. It also contains an empirical paper on the relationship between uncertainty of patent approval of artificial intelligence applications and the Supreme Court's decision in Alice Corp. v. CLS Bank International. Finally, the volume features a law-and-economics assessment of the Chinese financial system within the context of the trade-off between centralized control and rapid growth. This 30th volume of Research in Law and Economicsshowcases the cutting edge theoretical and empirical findings for researchers and professionals considering these complex issues intersecting law, technology, and economics.
Personalized Privacy Protection in Big Data by
This book presents the data privacy protection which has been extensively applied in our current era of big data. However, research into big data privacy is still in its infancy. Given the fact that existing protection methods can result in low data utility and unbalanced trade-offs, personalized privacy protection has become a rapidly expanding research topic. In this book, the authors explore emerging threats and existing privacy protection methods, and discuss in detail both the advantages and disadvantages of personalized privacy protection. Traditional methods, such as differential privacy and cryptography, are discussed using a comparative and intersectional approach, and are contrasted with emerging methods like federated learning and generative adversarial nets. The advances discussed cover various applications, e.g. cyber-physical systems, social networks, and location-based services. Given its scope, the book is of interest to scientists, policy-makers, researchers, and postgraduates alike.
Privacy-Preserving Computing: For Big Data Analytics and AI by
Privacy-preserving computing aims to protect the personal information of users while capitalizing on the possibilities unlocked by big data. This practical introduction for students, researchers, and industry practitioners is the first cohesive and systematic presentation of the field's advances over four decades. The book shows how to use privacy-preserving computing in real-world problems in data analytics and AI, and includes applications in statistics, database queries, and machine learning. The book begins by introducing cryptographic techniques such as secret sharing, homomorphic encryption, and oblivious transfer, and then broadens its focus to more widely applicable techniques such as differential privacy, trusted execution environment, and federated learning. The book ends with privacy-preserving computing in practice in areas like finance, online advertising, and healthcare, and finally offers a vision for the future of the field.
Kristen E. Busch, Cong. Rsch. Serv., R47569, Generative Artificial Intelligence and Data Privacy: A Primer (May 23, 2023), R47569 (congress.gov).
Laurie Harris, Cong. Rsch. Serv., IF12426, Generative Artificial Intelligence: Overview, Issues, and Questions for Congress (June 9, 2023), IF12426 (congress.gov)
מעודכן ל: 11.7.2024
כל האזכורים של מקורות יש לאזכר לפי כללי האזכור האחיד והבלובוק. סרטוני הדרכה על אזכור חקיקה ופסיקה.
|
