Visible to the public Biblio

Filters: Author is Kiyomoto, Shinsaku  [Clear All Filters]
Nakano, Yuto, Nakamura, Toru, Kobayashi, Yasuaki, Ozu, Takashi, Ishizaka, Masahito, Hashimoto, Masayuki, Yokoyama, Hiroyuki, Miyake, Yutaka, Kiyomoto, Shinsaku.  2021.  Automatic Security Inspection Framework for Trustworthy Supply Chain. 2021 IEEE/ACIS 19th International Conference on Software Engineering Research, Management and Applications (SERA). :45—50.
Threats and risks against supply chains are increasing and a framework to add the trustworthiness of supply chain has been considered. In this framework, organisations in the supply chain validate the conformance to the pre-defined requirements. The results of validations are linked each other to achieve the trustworthiness of the entire supply chain. In this paper, we further consider this framework for data supply chains. First, we implement the framework and evaluate the performance. The evaluation shows 500 digital evidences (logs) can be checked in 0.28 second. We also propose five methods to improve the performance as well as five new functionalities to improve usability. With these functionalities, the framework also supports maintaining the certificate chain.
Bajard, Jean-Claude, Fukushima, Kazuhide, Kiyomoto, Shinsaku, Plantard, Thomas, Sipasseuth, Arnaud, Susilo, Willy.  2021.  Generating Residue Number System Bases. 2021 IEEE 28th Symposium on Computer Arithmetic (ARITH). :86—93.
Residue number systems provide efficient techniques for speeding up calculations and/or protecting against side channel attacks when used in the context of cryptographic engineering. One of the interests of such systems is their scalability, as the existence of large bases for some specialized systems is often an open question. In this paper, we present highly optimized methods for generating large bases for residue number systems and, in some cases, the largest possible bases. We show their efficiency by demonstrating their improvement over the state-of-the-art bases reported in the literature. This work make it possible to address the problem of the scalability issue of finding new bases for a specific system that arises whenever a parameter changes, and possibly open new application avenues.
Tesfay, Welderufael B., Hofmann, Peter, Nakamura, Toru, Kiyomoto, Shinsaku, Serna, Jetzabel.  2018.  I Read but Don'T Agree: Privacy Policy Benchmarking Using Machine Learning and the EU GDPR. Companion Proceedings of the The Web Conference 2018. :163–166.
With the continuing growth of the Internet landscape, users share large amount of personal, sometimes, privacy sensitive data. When doing so, often, users have little or no clear knowledge about what service providers do with the trails of personal data they leave on the Internet. While regulations impose rather strict requirements that service providers should abide by, the defacto approach seems to be communicating data processing practices through privacy policies. However, privacy policies are long and complex for users to read and understand, thus failing their mere objective of informing users about the promised data processing behaviors of service providers. To address this pertinent issue, we propose a machine learning based approach to summarize the rather long privacy policy into short and condensed notes following a risk-based approach and using the European Union (EU) General Data Protection Regulation (GDPR) aspects as assessment criteria. The results are promising and indicate that our tool can summarize lengthy privacy policies in a short period of time, thus supporting users to take informed decisions regarding their information disclosure behaviors.
Fukushima, Keishiro, Nakamura, Toru, Ikeda, Daisuke, Kiyomoto, Shinsaku.  2018.  Challenges in Classifying Privacy Policies by Machine Learning with Word-based Features. Proceedings of the 2Nd International Conference on Cryptography, Security and Privacy. :62–66.

In this paper, we discuss challenges when we try to automatically classify privacy policies using machine learning with words as the features. Since it is difficult for general public to understand privacy policies, it is necessary to support them to do that. To this end, the authors believe that machine learning is one of the promising ways because users can grasp the meaning of policies through outputs by a machine learning algorithm. Our final goal is to develop a system which automatically translates privacy policies into privacy labels [1]. Toward this goal, we classify sentences in privacy policies with category labels, using popular machine learning algorithms, such as a naive Bayes classifier.We choose these algorithms because we could use trained classifiers to evaluate keywords appropriate for privacy labels. Therefore, we adopt words as the features of those algorithms. Experimental results show about 85% accuracy. We think that much higher accuracy is necessary to achieve our final goal. By changing learning settings, we identified one reason of low accuracies such that privacy policies include many sentences which are not direct description of information about categories. It seems that such sentences are redundant but maybe they are essential in case of legal documents in order to prevent misinterpreting. Thus, it is important for machine learning algorithms to handle these redundant sentences appropriately.

Tesfay, Welderufael B., Hofmann, Peter, Nakamura, Toru, Kiyomoto, Shinsaku, Serna, Jetzabel.  2018.  PrivacyGuide: Towards an Implementation of the EU GDPR on Internet Privacy Policy Evaluation. Proceedings of the Fourth ACM International Workshop on Security and Privacy Analytics. :15-21.

Nowadays Internet services have dramatically changed the way people interact with each other and many of our daily activities are supported by those services. Statistical indicators show that more than half of the world's population uses the Internet generating about 2.5 quintillion bytes of data on daily basis. While such a huge amount of data is useful in a number of fields, such as in medical and transportation systems, it also poses unprecedented threats for user's privacy. This is aggravated by the excessive data collection and user profiling activities of service providers. Yet, regulation require service providers to inform users about their data collection and processing practices. The de facto way of informing users about these practices is through the use of privacy policies. Unfortunately, privacy policies suffer from bad readability and other complexities which make them unusable for the intended purpose. To address this issue, we introduce PrivacyGuide, a privacy policy summarization tool inspired by the European Union (EU) General Data Protection Regulation (GDPR) and based on machine learning and natural language processing techniques. Our results show that PrivacyGuide is able to classify privacy policy content into eleven privacy aspects with a weighted average accuracy of 74% and further shed light on the associated risk level with an accuracy of 90%. This article is summarized in: the morning paper an interesting/influential/important paper from the world of CS every weekday morning, as selected by Adrian Colyer