Methods of Teachers' Personal Privacy Security Protection Based on Big Data Analysis. Issue 1 (February 2020)

• Record Type: Journal Article
• Authors: Zhang, Haichao
• Is Part Of: IOP conference series. Volume 750:Issue 1(2020)
• Publication Date: 2020-02
• Subjects: Big Data -- Teachers' -- Personal Privacy -- ORAM Technology -- Group Signature Technology; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/750/1/012231
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: With the advent of the era of big data, these diverse, maritime and high-speed terminal data will be calculated through cloud-based storage, sharing, and mining of information, cheaply, efficiently, and quickly. The creation and driving of new industries, new products, new services, and big data are constantly emerging, which has profoundly changed people's daily lives. The value of data in the era of big data is constantly being discovered and developed to provide a convenient and comfortable life. It brings unprecedented challenges to privacy protection. The collection of personal information is throughout life. The preservation of information is vulnerable to online attacks and leads to large-scale disclosure of personal information. During the operation of big data, problems such as illegal collection of privacy information, excessive analysis and illegal transactions occurred, which constantly threatened the stability of personal life and the security of personal property. The purpose of this article is to study teachers' privacy protection methods based on big data analysis. This paper uses the fusion technology of ORAM and group signatures to study the privacy protection of teachers. It aims to better protect the privacy of teachers in a big data environment. It also provides reference suggestions for other researchers and contributes to the protection of teachers' privacy.