With more data including personal information being hosted online such as cloud, privacy leakage is becoming one of serious concerns in online community. In practice, different temporal, spatial or application cases often demand different privacy protection solutions. Most of traditional approaches are case by case or based on a specific application circumstance. Therefore, we need to develop a systematic and quantized privacy characterization towards systematic computing model describing the relationships between protection level, profit and loss as well as the complexity of integrated privacy protection models Privacy computing is emerging as a paradigm to systematically scope privacy protection and related techniques.
Specifically, this book welcomes two categories of chapters: (1) invited articles from qualified experts; and (2) contributed papers from an open call with a list of addressed topics. Topics of interest include but not limited to:
Note: Papers are gradually reviewed and accepted from 1st Sep 2019 until submission deadline or when we reach to the maximum number of pages agreed with Springer. Authors are suggested to submit their chapters as early as possible and consult with one of the editors prior to the paper submission to validate the relevance and suitability of their submissions.
Submission by E-Mail Only, To submit, please email your paper to adehghan [ at ] uoguelph.ca .
All accepted papers will be published as contributed chapters to a book titled “Handbook of Big Data Privacy “. Please carefully format your manuscript in accordance with Springer Book Manuscript Guideline.
For any inquiries regarding your submission or suitability of your paper, please feel free to contact book editors: