PDAP-GAN: Generative Adversarial Network for Power Data Anonymization Protection
Abstract
References
Index Terms
- PDAP-GAN: Generative Adversarial Network for Power Data Anonymization Protection
Recommendations
Geolocated Data Generation and Protection Using Generative Adversarial Networks
Modeling Decisions for Artificial IntelligenceAbstractData mining techniques allow us to discover patterns in large datasets. Nonetheless, data may contain sensitive information. This is especially true when data is georeferenced. Thus, an adversary could learn about individual whereabouts, points of ...
Effective De-identification Generative Adversarial Network for Face Anonymization
MM '21: Proceedings of the 29th ACM International Conference on MultimediaThe growing application of face images and modern AI technology has raised another important concern in privacy protection. In many real scenarios like scientific research, social sharing and commercial application, lots of images are released without ...
Comments
Information & Contributors
Information
Published In

Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Science and Technology Project of State Grid Jiangsu Electric Power Co., Ltd. Research
Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 17Total Downloads
- Downloads (Last 12 months)17
- Downloads (Last 6 weeks)4
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format