Privacy-Preserving Computing for Analytics and Mining
A special issue of Inventions (ISSN 2411-5134). This special issue belongs to the section "Inventions and Innovation in Design, Modeling and Computing Methods".
Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 15899
Special Issue Editor
Interests: privacy-preserving data publishing (PPDP); information security; information privacy; COVID-19 privacy; data mining; social network analysis and mining; machine learning; zero knowledge proofs; differential privacy; confidential computing; secure multiparty computation; statistical disclosure control
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Due to the recent proliferation in digital solutions such as social networks (SN), recommender systems, cyberphysical social systems (CPSS), and the Internet of Things (IoT), a large amount of data about an individual is collected and processed. These collected data often contain information about an individual’s identity (i.e., demographics), salary, health status, social activities, etc. On one hand, this data is regarded as an oil of the economy when processed using advanced data mining and analytics tools. On the other hand, mishandling such data can spark public criticism and anger if privacy protection is not ensured. Making sense of such data while at the same time preserving privacy is another longstanding challenge in academia and research. To strike a balance between utility and privacy, much research has been proposed. Nevertheless, technical challenges and open research gaps remain in the area of privacy-preserving computing for analytics and mining purposes leverage individuals' data.
This Special Issue aims to present recent advances in tools, methods, techniques, prototypes, case studies, and technologies to improve privacy preservation leveraging traditional and AI technologies.
Topics of interest include but are not limited to:
- Privacy-preserving computing;
- Privacy-preserving data publishing;
- Privacy-preserving data mining;
- Anonymization;
- Information privacy;
- Social network privacy preservation;
- Analytics techniques with privacy guarantees;
- Emerging privacy threats due to the adoption of social networks;
- IoT privacy challenges and innovative solutions;
- Big data privacy and security;
- Cloud computing privacy issues and solutions;
- Encryption techniques to protect the contents of personal data;
- Advance privacy protection techniques pertinent to the COVID-19 era;
- Privacy issues in the cyberphysical social systems (CPSS);
- Case studies about people’s perception about privacy in different regions;
- Emerging privacy issues due the digitization across the globe.
Dr. Majeed Abdul
Guest Editor
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. Inventions 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 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.
Keywords
- Anonymization
- Utility
- Privacy
- Microdata
- Statistical disclosure control
- Identity disclosure
- Sensitive information disclosure
- Association rule hiding
- Data generalization
- Social networks
- Data owners/holders
- Information privacy
- Generalization
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