Multimedia Big Data Privacy and Processing Based on Compressive Sensing

  in Special Issue   Posted on May 5, 2020

Information for the Special Issue

Special Issue Call for Papers:

Multimedia data that include image and video are the biggest ‘big’ data and their applications, such as social media, healthcare, and video surveillance, are ubiquitous. However, multimedia big data (MMBD) face several privacy issues: the privacy issues of MMBD themselves through information leakage and data tampering, and the privacy issues during the processing of MMBD, such as data acquisition, data storage, and data analysis.

Compressive sensing (CS) as an emerging light-weight encryption mechanism is receives widespread attention. CS can bring a confidentiality guarantee when random measurement matrix acts as a key and would provide privacy protection for MMBD. For example, CS-based encryption is for data confidentiality and CS-based watermarking is for data integrity. Moreover, CS requires fewer samples to achieve the same reconstruction performance for the signal compared with the traditional Shannon-Nyquist sampling. CS also has the strong robustness of recovering the signal even if samples are contaminated by noise. Thus, CS has very good characteristics for privacy-preserving MMBD processing applications. Examples of potential privacy-preserving processing include low-complexity sampling for data acquisition; compression during sampling for data storage; and reconstruction robustness for data analysis.

This Special Issue aims at collecting different architectures, solutions, mechanisms to understand and exploit the potential of CS for multimedia big data privacy and processing.

Topics of interest include, but are not limited to:

  • MMBD encryption based on CS
  • MMBD steganography based on CS
  • MMBD watermarking, information hiding, and hashing based on CS
  • MMBD authentication and forensics based on CS
  • Privacy-preserving MMBD aggregation
  • Privacy-preserving MMBD representation and coding based on CS
  • Privacy-preserving MMBD recognition based on CS measurements
  • Privacy-preserving MMBD classification, restoration, and optimization based on CS and sparsity

Important Dates:

Paper Submission: September 30, 2020
First round of reviews: November 30, 2020
Submission of revised papers: January 15, 2021
Second round of reviews: March 15, 2021
Final Papers Submission: April 30, 2021

Guest Editors:

Yushu Zhang (lead)
Nanjing University of Aeronautics and Astronautics, China

Leo Yu Zhang
Deakin University, Australia

Junxin Chen
University of Macau, Macau SAR

Guang Hua
Wuhan University, China

Shangwei Guo
Nanyang Technological University, Singapore

Pengfei Hu

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