SUMMARYInternet of Things (IoT) has been widely applied in various fields. IoT data can also be put to cloud, but there are still concerns regarding security and privacy. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is attracted attention in cloud storage as a suitable encryption scheme for confidential data share and transmission. In CP-ABE, the secret key of a user is associated with a set of attributes; when attributes satisfy the access structure, the ciphertext is able to be decrypted. It is necessary that multiple authorities issue and manage secret keys independently. Authorities that generate the secret key can be regarded as managing the attributes of a user in CP-ABE. CP-ABE schemes that have multiple authorities have been proposed. The other hand, it should consider that a user's operation at the terminals is not necessary when a user drop an attribute and key is updated and the design of the communication system is a simple. In this paper, we propose CP-ABE scheme that have multiple key authorities and can revoke attribute immediately with no updating user's secret key for attribute revocation. In addition, the length of ciphertext is fixed. The proposed scheme is IND-CPA secure in DBDH assumption under the standard model. We compare the proposed scheme and the other CP-ABE schemes and show that the proposed scheme is more suitable for cloud storage. key words: ciphertext-policy attribute-based encryption, multiple key authorities, attribute revocation, forward secrecy

Cyber attacks targeting specific victims use multiple intrusion routes and various attack methods. In order to combat such diversified cyber attacks, Threat Intelligence is attracting attention. Attack activities, vulnerability information and other threat information are gathered, analyzed and organized in threat intelligence and it enables organizations to understand their risks. Integrated analysis of the threat information is needed to compose the threat intelligence. Threat information can be found in incident reports published by security vendors. However, it is difficult to analyze and compare their reports because they are described in various formats defined by each vendor. Therefore, in this paper, we apply a modeling framework for analyzing and deriving the relevance of the reports from the views of similarity and relation between the models. This paper presents the procedures of modeling incident information described in the reports. Moreover, as case studies, we apply the modeling method to some actual incident reports and compare their models.

Data mining entails the discovery of unexpected but reusable knowledge from large unorganized datasets. Among the many available data-mining algorithms, association rule mining (ARM) is very common. It was developed to aggregate all data into one site and subsequently mine them. In recent years, organizations in different fields have been required to collaborate to create new value. However, data mining among and within organizations has raised privacy and confidentiality concerns. In our scheme, parties cannot share anything other than the number of records, including the candidate itemset. This study focuses on the private-set intersection instead of the scalar product and shows that this intersection enables organizations to execute ARM on vertically partitioned data, allowing flexible information sharing while preserving privacy without increasing communication and computation costs. Furthermore, we focus on the fact that the number of protocol rounds among parties can be reduced and present three use cases in which the proposed scheme works more effectively than the existing schemes.

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