| Title | Privacy-Preserving Deep Learning Models for Law Big Data Feature Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Yuan, Xu, Zhang, Jianing, Chen, Zhikui, Gao, Jing, Li, Peng |
| Conference Name | 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) |
| Keywords | Big Data, big data privacy, cryptography, Data models, data privacy, Deep Learning, Differential privacy, homomorphic encryption, human factors, Internet of Things, IoT, Knowledge extraction, law Big data feature learning, learning (artificial intelligence), Metrics, neural nets, privacy, privacy-preserving deep learning models, privacy-preserving feature learning, pubcrawl, Resiliency, Scalability, Servers, social networking (online), social networks |
| Abstract | Nowadays, a massive number of data, referred as big data, are being collected from social networks and Internet of Things (IoT), which are of tremendous value. Many deep learning-based methods made great progress in the extraction of knowledge of those data. However, the knowledge extraction of the law data poses vast challenges on the deep learning, since the law data usually contain the privacy information. In addition, the amount of law data of an institution is not large enough to well train a deep model. To solve these challenges, some privacy-preserving deep learning are proposed to capture knowledge of privacy data. In this paper, we review the emerging topics of deep learning for the feature learning of the privacy data. Then, we discuss the problems and the future trend in deep learning for privacy-preserving feature learning on law data. |
| DOI | 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00035 |
| Citation Key | yuan_privacy-preserving_2019 |
Privacy-Preserving Deep Learning Models for Law Big Data Feature Learning