Privacy-Enhancing Technologies of Data for Sustainable and Secure Cooperation

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (30 March 2024) | Viewed by 307

Special Issue Editors

School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: information security; data security; cyber security; privacy-enhancing technology
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Guest Editor
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: multimedia communications; network security; internet of things

Special Issue Information

Dear Colleagues,

Sustainable development is a multidisciplinary and complex topic in many areas. To achieve sustainable development in the information security filed, privacy-enhancing technology is essential. By implementing privacy-enhancing technologies, organizations can overcome barriers to data sharing and collaboration. Concerns about data misuse and breaches are alleviated, fostering a culture of trust and cooperation. Privacy-enhancing technologies enable organizations to share data without compromising the privacy of individuals, which is particularly important in sensitive industries such as healthcare, finance, and telecommunications.

Privacy-enhancing technologies, which are digital solutions that allow information to be collected, processed, analysed, and shared while protecting data confidentiality and privacy, enable organisations to conduct joint data analysis in a privacy-friendly manner. The true power of privacy-enhancing technology is in keeping data “hidden” from researchers while at the same time enabling the analysis of that data. These technologies could unlock new forms of collaboration and new norms in the responsible use of personal data. They may enable more collaboration across entities, sectors, and borders to help tackle shared challenges, helping drive solutions in areas such as health care, climate change, financial crime, human trafficking, and pandemic response.

Privacy-enhancing technologies offer several specific techniques to enhance privacy. Encryption techniques transform data into unreadable formats, ensuring that only authorized parties can access and decrypt the information. Anonymization methods remove personally identifiable information from datasets, making it challenging to identify specific individuals. Differential privacy techniques introduce noise or randomness to data analysis, preventing the identification of individual records.

In this Special Issue, we invite scholars and practitioners to submit their original research or review articles. Research areas may include, but are not limited to, the following:

  • Secure Multi-Party Computation (MPC);
  • Anonymization methods;
  • Differential privacy;
  • Sustainable development of network;
  • Blockchain security;
  • AI security;
  • Image security;
  • Voice security;
  • Sustainable data collection, processing, transmission and security;
  • Sustainable data cooperation;
  • Homomorphic encryption;
  • Data masking.

We look forward to receiving your contributions.

Dr. Yi Sun
Dr. Shujie Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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. Big Data and Cognitive Computing is an international peer-reviewed open access monthly 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 1800 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.


  • secure multi-party computation (MPC)
  • anonymization methods
  • differential privacy
  • sustainable development of network
  • blockchain security
  • AI security
  • image security
  • voice security
  • sustainable data collection, processing, transmission and security
  • sustainable data cooperation
  • homomorphic encryption
  • data masking

Published Papers

There is no accepted submissions to this special issue at this moment.
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