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Titlebook: Data and Applications Security and Privacy XXXIV; 34th Annual IFIP WG Anoop Singhal,Jaideep Vaidya Conference proceedings 2020 IFIP Intern

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21#
發(fā)表于 2025-3-25 04:54:27 | 只看該作者
22#
發(fā)表于 2025-3-25 08:43:28 | 只看該作者
Security Enumerations for Cyber-Physical Systems integrate threat information. While traditional IT systems and vulnerabilities are covered by security enumerations, this does not apply to Cyber-Physical Systems?(CPS). In particular, complexity and interdependencies of components within these systems demand for an extension of current enumeration
23#
發(fā)表于 2025-3-25 14:46:25 | 只看該作者
Inference-Proof Monotonic Query Evaluation and View Generation Reconsideredowner, communicating with a semi-honest partner by means of their message exchanging computing agents according to some agreed interaction protocols. Such protocols include closed-query evaluation and view generation by the information system agent under the control of the information owner, and the
24#
發(fā)表于 2025-3-25 19:00:07 | 只看該作者
25#
發(fā)表于 2025-3-25 23:05:37 | 只看該作者
Effective Access Control in Shared-Operator Multi-tenant Data Stream Management Systemse requirements of these applications. DSMSs were developed to efficiently execute continuous queries (CQs) over incoming data. Multiple CQs can be optimized together to form a query network by sharing operators across CQs. DSMSs are also required to enforce access controls over operators according t
26#
發(fā)表于 2025-3-26 00:48:20 | 只看該作者
27#
發(fā)表于 2025-3-26 07:25:25 | 只看該作者
28#
發(fā)表于 2025-3-26 09:11:05 | 只看該作者
Non-interactive Private Decision Tree Evaluationcision tree model and a client interested in classifying its private attribute vector using the server’s private model. The goal of the computation is to obtain the classification while preserving the privacy of both—the decision tree and the client input. After the computation, the client learns th
29#
發(fā)表于 2025-3-26 13:26:45 | 只看該作者
Privacy-Preserving Anomaly Detection Using Synthetic Dataa analysis methods. While there have been impressive advances in machine learning and similar domains in recent years, this also gives rise to concerns regarding the protection of personal and otherwise sensitive data, especially if it is to be analysed by third parties, e.g. in collaborative settin
30#
發(fā)表于 2025-3-26 20:02:39 | 只看該作者
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