Big Data and Machine Learning with Privacy Enhancing Tech 2023

Workshop on Big Data and Machine Learning with Privacy Enhancing Tech

In conjunction with IEEE Big Data Service 2023 (IEEE BDS 2023)

Summary of the special session

As privacy concerns become increasingly important, all types of
stakeholders (from businesses, researchers, entrepreneurs) must find ways to
incorporate privacy-enhancing technologies (PET) into their solutions –
especially in machine learning and big data-based projects. While they are
eager to introduce innovative solutions to their research community or market,
they often face challenges due to stringent privacy regulations. Without
adequate privacy protection, many projects and services are at risk of being
shut down, despite their potential benefits and novelty.

 To address these challenges, either academic researchers or industrial
companies, they all must invest in PET to help preserve privacy while still
delivering valuable insights from big data using machine learning algorithms.
By integrating PET into their projects, they can not only comply with privacy
regulations but also show the trustworthiness to their customers and
stakeholders. The use of PET can also help companies, researchers, entrepreneurs
to mitigate the risks associated with data breaches and cyber attacks, which
can have severe consequences.

To work together by complementing with each other’s expertise,
researchers, businesses, and PET experts can develop effective solutions that meet
the needs of customers, regulators, and other stakeholders. In this workshop,
we aim at connecting all these crucial needs and encourage more scientific
contributions to this domain.

 Scope and Topics

Following topics are of our interests (but not limited to):

  • Big data and privacy-preserving data science
  • Privacy-preserving machine learning
  • Natural language processing with privacy preservation
  • Data management of big data with PET
  • Machine Learning with PET
  • Graph networks with privacy preservation
  • Multilayer graph network analysis with privacy


Welcome to submit your research work (8-page full research paper or 5-page short research papers/demo papers, or 2-page posters). All accepted papers will be published by IEEE Computer Society Press (EI-Index) and included in IEEE Digital Library.

For more information about submission, click here.

Important Dates

Deadline: June 1st, 2023

Notification: June 10th, 2023


  • Lili Jiang, Department of Computing Science, Umeå University, Sweden
  • Shashi Gowda, Devr INC, USA
  • Sharma Chakravarthy, The University of Texas at Arlington, USA
For more details, please contact Assoc. Prof. Lili Jiang (email:

Call For Papers

You may download the workshop's Call For Papers from the link below.

CFP File

List of Accepted Papers

Click the link below for the list of accepted papers

Accepted Papers