Broadcast encryption allows a sender to securely distribute messages to a dynamically changing set of users over an insecure channel. In a public key broadcast encryption (PKBE) scheme, this encryption is performed in the public key setting, where the public key is stored in a user's device, or directly transmitted to the receivers along with ciphertexts. In this paper, we propose two PKBE schemes for stateless receivers which are transmission-efficient. A distinctive feature in our first construction is that, different than existing schemes in the literature, only a fraction of the public key related to the set of intended receivers is required in the decryption process. This feature results in the first PKBE scheme with ( ) transmission cost and (1) user storage cost for revoked users. Our second construction is a generalized version of the first one providing a tradeoff between ciphertext size and public key size. With appropriate parametrization, we obtain a PKBE scheme with ( ) transmission cost and (1) user storage cost for any large set of users. The transmission cost of our second scheme is at least 30\% less than that of the recent result of Boneh et al.'s PKBE scheme, which is considered as being the current state-of-the-art. By combining the two proposed schemes, we suggest a PKBE scheme that achieves further shortened transmissions, while still maintaining (1) user storage cost. The proposed schemes are secure against any number of colluders and do not require costly re-keying procedures followed by revocation of users.
Product obsolescence occurs in every production line in the industry as better-performance or cost-effective products become available. A proactive strategy for obsolescence allows firms to prepare for such events and reduces the manufacturing loss, which eventually leads to positive customer satisfaction. We propose a machine learning-based algorithm to forecast the obsolescence date of electronic diodes, which has a limitation on the amount of data available. The proposed algorithm overcomes these limitations in two ways. First, an unsupervised clustering algorithm is applied to group the data based on their similarity and build independent machine-learning models specialized for each group. Second, a hybrid method including several reliable techniques is constructed to improve the prediction accuracy and overcome the limitation of the lack of data. It is empirically confirmed that the prediction accuracy of the obsolescence date for the electrical component data is improved through the proposed clustering-based hybrid method.
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