Title
Analysis of Privacy Compliance by Classifying Multiple Policies on the Web
Abstract
Companies and organizations inform users of how they handle personal data through privacy policies on their websites. Particular information, such as the purposes of collecting personal data and what data are provided to third parties is required to be disclosed by laws and regulations. An example of such a law is the Act on the Protection of Personal Information in Japan. In addition to privacy policies, an increasing number of companies are publishing security policies to express compliance and transparency of corporate behavior. However, it is challenging to update these policies against legal requirements due to the periodic law revisions and rapid business changes. In this study, we developed a method for analyzing privacy policies to check whether companies comply with legal requirements. In particular, the proposed method classifies policy contents using a convolutional neural network and evaluates privacy compliance by comparing the classification results with legal requirements. In addition, we analyzed security policies using the proposed method, to confirm whether the combination of privacy and security policies contributes to privacy compliance. In this study, we collected and evaluated 1,304 privacy policies and 140 security policies for Japanese companies. The results revealed that over 90% of privacy policies sufficiently describe the handling of personal information by first parties, user rights, and security measures, and over 90% insufficiently describe the data retention and specific audience. These differences in the number of descriptions are dependent on industry guidelines and business characteristics. Moreover, security policies were found to improve the compliance rates of 46 out of 140 companies by describing security practices not included in privacy policies.
Year
DOI
Venue
2022
10.1109/COMPSAC54236.2022.00276
2022 IEEE 46TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2022)
Keywords
DocType
Citations 
Privacy Compliance, Privacy Policy, Security Policy, Convolutional Neural Network
Conference
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Keika Mori100.34
Tatsuya Nagai200.34
Yuta Takata300.34
Masaki Kamizono400.34