Publication:
Privacy-Preserving Machine Learning as a Tool for Secure Personalized Information Services

Дата
2020
Journal Title
Journal ISSN
Volume Title
Издатель
Научные группы
Организационные подразделения
Организационная единица
Институт интеллектуальных кибернетических систем
Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.
Выпуск журнала
Аннотация
© 2020 The Authors. Published by Elsevier B.V.The article deals with the problems of cryptographic protection of data processing algorithms and techniques. They are novel techniques allowing to process private information without disclosing it to persons engaged in processing. One of the main applications of such security tools is the creation of personalized information services, which opens up new opportunities for business and reduces the risks of unauthorized access to personal data. We review important building blocks for cryptographic protection of data processing, such as zero-knowledge proofs, secure multi-party computations, and homomorphic encryption. Often, personalized information services are based on data mining and machine learning, so privacy-preserved machine learning is a very important building block for them. We analyze the concept of differential privacy which serves as the basis for privacy-preserving machine learning and some other cryptographic schemes. At the end of the paper, we forecast the perspectives of encrypted data processing.
Описание
Ключевые слова
Цитирование
Zapechnikov, S. Privacy-Preserving Machine Learning as a Tool for Secure Personalized Information Services / Zapechnikov, S. // Procedia Computer Science. - 2020. - 169. - P. 393-399. - 10.1016/j.procs.2020.02.235
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