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Titlebook: Applications and Techniques in Information Security; 13th International C Srikanth Prabhu,Shiva Raj Pokhrel,Gang Li Conference proceedings

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樓主: Iodine
31#
發(fā)表于 2025-3-26 23:25:59 | 只看該作者
Husserl and the Problem of Ontology,sed recommendation, considering interest of a group of people is proposed here. The system also ensures privacy of the users by generating hash code of the interest provided by the group. The proposed scheme performs well with a prediction accuracy of 83% for a maximum of four users with TMDB database.
32#
發(fā)表于 2025-3-27 05:06:27 | 只看該作者
33#
發(fā)表于 2025-3-27 05:52:01 | 只看該作者
34#
發(fā)表于 2025-3-27 10:08:04 | 只看該作者
35#
發(fā)表于 2025-3-27 14:29:31 | 只看該作者
https://doi.org/10.1007/978-3-642-58682-8ional diffusion layer. In this work, we propose two different MixColumns like matrices to the AES block cipher that secures the cipher against Differential Fault Analysis attacks. The attack complexity is increased to . with our proposed matrix.
36#
發(fā)表于 2025-3-27 19:11:12 | 只看該作者
The G?ttingen Period (1915–1933) cyber attacks by analysing the data logs and compared with the non-federated learning techniques on the same data. It is very evident from the experiment that Federated Learning is very effective in detecting these attacks by preserving the privacy of the victim organisations/systems.
37#
發(fā)表于 2025-3-28 00:04:20 | 只看該作者
A Better MixColumns Matrix to?AES Against Differential Fault Analysis Attackional diffusion layer. In this work, we propose two different MixColumns like matrices to the AES block cipher that secures the cipher against Differential Fault Analysis attacks. The attack complexity is increased to . with our proposed matrix.
38#
發(fā)表于 2025-3-28 02:34:19 | 只看該作者
Intrusion Detection Using Federated Learning cyber attacks by analysing the data logs and compared with the non-federated learning techniques on the same data. It is very evident from the experiment that Federated Learning is very effective in detecting these attacks by preserving the privacy of the victim organisations/systems.
39#
發(fā)表于 2025-3-28 09:49:09 | 只看該作者
https://doi.org/10.1007/978-3-540-75544-9tributes will prompt privacy leakage. Choosing private data from a list of attributes is decided by the publisher, and it undoubtedly changes from dataset to dataset. The need for dynamically choosing and informing systems about a quasi and a non-quasi attribute remains a challenging task. Presently
40#
發(fā)表于 2025-3-28 11:49:16 | 只看該作者
https://doi.org/10.1007/978-3-540-75544-9of tampering attack is to recover the key by altering some regions of the memory. Such attack may also appear when the device is stolen or viruses has been introduced. Non-malleable codes are used to protect the secret information from tampering attacks. The secret key can be encoded using non-malle
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