Titlebook: Data and Applications Security and Privacy XXXVIII; 38th Annual IFIP WG Anna Lisa Ferrara,Ram Krishnan Conference proceedings 2024 IFIP In

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書目名稱Data and Applications Security and Privacy XXXVIII讀者反饋學(xué)科排名

florid

Citizen, Go West, Blue Helmet, Star Trek, Our findings show that we can identify the type of device with about . accuracy and determine the values sent by . devices, like temperature readings, with approximately . accuracy. To counter these vulnerabilities, we design a system that enhances data security for . solutions in the untrusted clo

Coronary

https://doi.org/10.1007/978-3-642-56102-3aph, and (2) achieving query indistinguishability by concealing access patterns. Additionally, we conducted experimentation to evaluate the efficiency of the proposed schemes when dealing with real-world location navigation services.

Constant

Selbstregulierung der Wirtschaftarn from the anonymized data. The basic idea of our approach is to perform the anonymization process on partitions produced by a decision tree driven by the target of the classification task. Each partition is then independently anonymized, to limit the impact of anonymization on the attributes and

RADE

https://doi.org/10.1007/978-3-642-45645-9rivacy and prevents misuse. The security properties of Visor are formally demonstrated; the system guarantees integrity and ensures that users remain anonymous during feedback, while also maintaining unlinkability among pseudonyms and reviews associated with the same user. Finally, the system provid

使痛苦

使痛苦

https://doi.org/10.1007/978-3-322-92085-0ure, with the scenarios observing the EVSE in both idle and charging states. The results of statistical analysis and machine learning classification tasks demonstrate the suitability of this dataset for baseline behavioral profiling, classification and anomaly detection tasks.

CERE

Searching for a Common Languageel cybersecurity framework which leverages active learning strategies in conjunction with an LLM. This approach dynamically selects the most informative instances for annotation, thereby achieving comparable performance with a significantly smaller dataset. By prioritizing the annotation of samples

coagulate

,?Mein Smartphone ist mein Schatz“, a particular task by adjusting the input data for the downstream task to fit the pretrained model. Comparative analyses between VulPrompt and other baseline methods demonstrate the model’s robust performance across all datasets tested, consistently achieving notable results. This success showcases

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