Privacy-Preserving Solutions and Technologies for the Big Data Era

Dear Colleagues,

In the big data era, the rapid growth of data collection technologies (e.g., mobile phones, sensors, wearable devices, visual sensing, etc.) has resulted in large-scale data collection and processing. However, sensitive information in such large-scale datasets has raised significant concerns about data privacy and security. In AI developments, privacy also attracts considerable attention to prevent model learning from unnecessary or privacy-endangering information. In recent years, plenty of privacy-preserving solutions have been developed, ranging from conventional anonymization to synthetic data and federated learning approaches. However, solving privacy/utility problems is still very challenging due to stringent privacy requirements and precise analytics needs. This Special Issue aims to attract recent developments in privacy-preserving solutions and technologies for the big data era, specifically focusing on data outsourcing and AI development scenarios. We solicit papers that touch upon the diverse methods, solutions, and techniques that are devised for various data modalities, specific attacks, AI models, and/or use cases. The purpose of this issue is to attract high-quality papers that offer targeted solutions for privacy preservation in diverse computing paradigms (e.g., cloud, edge, fog, IoT, industrial domains, AI models, etc.). The scope of this Special Issue includes synthetic data methods/use cases, differential privacy, federated learning, personalized differential privacy, data-centric anonymization schemes for multi-dimensional data, unlearning methods, privacy methods for diverse data modalities (tables, graphs, streams, etc.), privacy studies on various threat models, and use-case-specific privacy technologies. The Special Issue will publish high-quality papers that can assist the privacy and database community in understanding next-generation privacy requirements and the corresponding solutions.

Dr. Abdul Majeed
Prof. Dr. Seong Oun Hwang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• data anonymization
• differential privacy
• federated learning
• synthetic data
• machine learning-aided anonymization
• encryption
• privacy-preserving ML/AI
• hybrid privacy methods
• data-centric privacy methods
• privacy methods for diverse computing paradigms
• privacy methods for diverse data modalities
• privacy methods for data sharing
• new privacy/utility quantification methods
• privacy methods for poor quality datasets
• sampling-based privacy methods
• privacy protection in the lifecycle of AI applications
• de-anonymization methods