Traditionally, top-level domains (TLDs) are managed by the Internet corporation for assigned names and numbers (ICANN), and the domain names under them are managed by registrars. Against such a centralized management, a blockchain naming service (BNS) has been proposed to manage TLDs on blockchains without authority intervention. BNS users can register TLD strings as non-fungible tokens and manage the TLD root zone. However, such a decentralized management results in the introduction of a new security issue, BNS TLD name collision, wherein the same TLD is registered in several different BNSs. In this study, we investigated BNS TLD name collisions by analyzing TLDs registered on two BNSs: Handshake and Decentraweb. Specifically, we collected TLDs registered in Handshake and Decentraweb and the associated data, and analyzed the data registration status of BNS TLDs and BNS TLD name collisions. The analysis of 11,595,406 Handshake and 11,889 Decentraweb TLDs revealed 6,973 BNS TLD name collisions. In particular, lastname TLDs, which are intended for use as person names, yielded a large number of registered domain names. In addition, the analysis identified 10 name collisions between the BNS and operational ICANN TLDs. Further, the ICANN TLD candidates under review also had name collisions against the BNS TLDs. Consequently, based on the characteristics of these name collisions and discussions in BNS communities, we considered countermeasures against BNS TLD name collisions. For the further development of BNSs, we believe that it is essential to discuss with the existing Internet communities and coexist with the existing Internet.

Companies and organizations inform users of how they handle personal data through privacy policies on their web sites. Particular information, such as the purposes of col-lecting 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.