Youyang Qu - Personalized Privacy Protection in Big Data

Aims and Goals:

Building and promoting the field of data science and analytics in terms of publishing work on theoretical foundations, algorithms and models, evaluation and experiments, applications and systems, case studies, and applied analytics in specific domains or on specific issues.

Specific Topics:

Suggested Titles for Proposals:

More information about this series at http://www.springer.com/series/15063

This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd.

The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Over the past few decades, massive volumes of data in digital form have been generated, collected, and published with the fast booming of high-performance computing devices and communicating infrastructures, which brings forward the prosperity of this big data era. Organizations, institutions, and governments are playing the key roles for collecting, storing, and sharing data. For example, social networks indicate interest and social connections of users, smart wearable devices record health status of individuals, educational institutions analyse learning patterns of students, and vehicular networks collect the daily routine of drivers. By leveraging the massive amounts of data, governments and corporations have the opportunity to improve the quality of services, bring financial benefits, and potentially create social values using diverse data processing techniques, such as machine learning, data mining, artificial intelligence, and so on. A popular real-world application scenario is that the statistics of a series of medical records is able to significantly lift the diagnosis accuracy. However, almost all collected datasets contain sensitive information implicitly or explicitly, although basic anonymization solutions have been deployed to hide the unique identifiers. Besides, the linkability of different data sources poses further challenges to privacy protection. Thus, privacy preservation has become a crucial issue that needs to be addressed in this big data age.

Personalized privacy protection is a set of emerging technologies that can personalize the privacy protection based on various indexes, such as social distance in social networks and the trade-off between privacy protection and data utility. It attracts extensive interest from both academia and industry. It can be integrated with almost all the existing mainstream privacy protection frameworks, including differential privacy, clustering-based methods, and machine learning-based models, which makes it potentially applicable in many real-world scenarios.