Titlebook: Data and Applications Security and Privacy XXXV; 35th Annual IFIP WG Ken Barker,Kambiz Ghazinour Conference proceedings 2021 IFIP Internat

compassion

BET

Conference proceedings 20061st editionthe program’s code) and black-box attacks (i.e., attacks where have input/output access to the program’s code). Our starting point is cryptographic program obfuscation, which guarantees some provable security against inspection attacks, in the sense that any such attack is not significantly more suc

Grating

Kaleidoscope

D. P. Landau,S. P. Lewis,H. -B. Schüttlerlopers use user stories to write code, these user stories are better representations of the actual code than that of the high-level product documentation. In this paper, we develop an automated approach using machine learning to generate access control information from a set of user stories that des

睨視

H. De Raedt,K. De Raedt,K. Michielsenctual property, such as ML source code, model, or datasets. State-of-the-art solutions based on homomorphic encryption incur a large performance overhead. Hardware-based solutions, such as trusted execution environments (TEEs), significantly improve the performance in inference computations but stil

屈尊

H. De Raedt,K. De Raedt,K. Michielsenent malicious attacks in advance. According to the diversity of the software platforms that people use, the malware also varies pretty much, for example: Xcode Ghost on iOS apps, FakePlayer on Android apps, and WannaCrypt on PC. Moreover, most of the time people ignore the potential security threats

Femish

變態(tài)

https://doi.org/10.1007/978-3-8348-2376-2ned momentum. To solve this problem, anonymization techniques such as .-anonymity, .-diversity, and .-closeness have been used to generate anonymized datasets for training classifiers. While these techniques provide an effective means to generate anonymized datasets, an understanding of how their ap

引起

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