標(biāo)題: Titlebook: Data and Applications Security and Privacy XXXV; 35th Annual IFIP WG Ken Barker,Kambiz Ghazinour Conference proceedings 2021 IFIP Internat [打印本頁] 作者: fumble 時(shí)間: 2025-3-21 19:26
書目名稱Data and Applications Security and Privacy XXXV影響因子(影響力)
書目名稱Data and Applications Security and Privacy XXXV影響因子(影響力)學(xué)科排名
書目名稱Data and Applications Security and Privacy XXXV網(wǎng)絡(luò)公開度
書目名稱Data and Applications Security and Privacy XXXV網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data and Applications Security and Privacy XXXV被引頻次
書目名稱Data and Applications Security and Privacy XXXV被引頻次學(xué)科排名
書目名稱Data and Applications Security and Privacy XXXV年度引用
書目名稱Data and Applications Security and Privacy XXXV年度引用學(xué)科排名
書目名稱Data and Applications Security and Privacy XXXV讀者反饋
書目名稱Data and Applications Security and Privacy XXXV讀者反饋學(xué)科排名
作者: 出血 時(shí)間: 2025-3-22 00:16 作者: Affection 時(shí)間: 2025-3-22 03:15
https://doi.org/10.1007/978-3-030-81242-3access control; artificial intelligence; authentication; computer networks; computer science; computer se作者: Systemic 時(shí)間: 2025-3-22 08:34
978-3-030-81241-6IFIP International Federation for Information Processing 2021作者: orient 時(shí)間: 2025-3-22 09:41 作者: Transfusion 時(shí)間: 2025-3-22 13:33 作者: Transfusion 時(shí)間: 2025-3-22 18:15
B. Ujfalussy,T. C. Schulthess,M. Stocksocol, the clients perturb their data locally with a randomized mechanism before sending it to the server for analysis. Many studies in the literature of LDP implicitly assume that the clients honestly follow the protocol; however, two recent studies show that LDP is generally vulnerable under malici作者: 貧困 時(shí)間: 2025-3-23 00:08
B. Schmittmann,J. T. Mettetal,R. K. P. Ziavolume-hiding structured encryption (.), particularly encrypted multi-maps (.), in which all queries should share the same (as the largest) response size unless the scheme is lossy. Meanwhile, note that the responses are originated from the actual ciphertexts outsourced to the server. Conventional w作者: 不要嚴(yán)酷 時(shí)間: 2025-3-23 01:38 作者: VEST 時(shí)間: 2025-3-23 08:56 作者: compassion 時(shí)間: 2025-3-23 11:25 作者: BET 時(shí)間: 2025-3-23 16:30
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作者: Grating 時(shí)間: 2025-3-23 21:38 作者: Kaleidoscope 時(shí)間: 2025-3-23 22:33
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作者: 睨視 時(shí)間: 2025-3-24 05:10
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作者: 屈尊 時(shí)間: 2025-3-24 06:43
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作者: Femish 時(shí)間: 2025-3-24 12:32 作者: 變態(tài) 時(shí)間: 2025-3-24 14:52
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作者: 引起 時(shí)間: 2025-3-24 23:05 作者: Encephalitis 時(shí)間: 2025-3-25 01:35 作者: mastoid-bone 時(shí)間: 2025-3-25 03:19 作者: 懸崖 時(shí)間: 2025-3-25 10:58
Simple Storage-Saving Structure for Volume-Hiding Encrypted Multi-mapsvolume-hiding structured encryption (.), particularly encrypted multi-maps (.), in which all queries should share the same (as the largest) response size unless the scheme is lossy. Meanwhile, note that the responses are originated from the actual ciphertexts outsourced to the server. Conventional w作者: coddle 時(shí)間: 2025-3-25 13:14 作者: 多產(chǎn)魚 時(shí)間: 2025-3-25 18:30
Distributed Query Evaluation over Encrypted Datambining different data collections for their elaboration and analysis. Due to the quick pace at which collected data grow, often the authorities collecting and owning such datasets resort to external third parties (e.g., cloud providers) for their storage and management. Data under the control of di作者: Dignant 時(shí)間: 2025-3-25 23:43 作者: IDEAS 時(shí)間: 2025-3-26 02:11
Encrypted-Input Obfuscation of Image Classifiersthe 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作者: 猛烈責(zé)罵 時(shí)間: 2025-3-26 04:58 作者: 該得 時(shí)間: 2025-3-26 11:39 作者: cinder 時(shí)間: 2025-3-26 16:27 作者: 杠桿支點(diǎn) 時(shí)間: 2025-3-26 20:42
PDF Malware Detection Using Visualization and Machine Learningent 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作者: circumvent 時(shí)間: 2025-3-26 21:47 作者: allergen 時(shí)間: 2025-3-27 03:01 作者: seroma 時(shí)間: 2025-3-27 06:22 作者: –FER 時(shí)間: 2025-3-27 10:12
G. Korniss,P. A. Rikvold,M. A. Novotnye, we propose new techniques to improve the outcome of differentially private learning and provide the privacy analysis of the overall solution. Through a comprehensive evaluation with large-scale network flow data, we demonstrate that our solution is capable of producing realistic network flows.作者: Commentary 時(shí)間: 2025-3-27 16:12 作者: minaret 時(shí)間: 2025-3-27 18:22 作者: 蚊子 時(shí)間: 2025-3-27 23:46 作者: Rejuvenate 時(shí)間: 2025-3-28 03:36 作者: Insul島 時(shí)間: 2025-3-28 08:33
Not a Free Lunch, But a Cheap One: On?Classifiers Performance on?Anonymized Datasetsy, we perform extensive experiments to verify how the classifiers performance changes when trained on an anonymized dataset compared to the original one, and evaluate the impact of classification algorithms, datasets properties, and anonymization parameters on classifiers’ performance.作者: 休閑 時(shí)間: 2025-3-28 13:44 作者: 分開 時(shí)間: 2025-3-28 14:47 作者: BET 時(shí)間: 2025-3-28 18:57 作者: 發(fā)酵 時(shí)間: 2025-3-29 02:05 作者: interpose 時(shí)間: 2025-3-29 04:12
Distributed Query Evaluation over Encrypted Dataations combining these sources hard. In this paper, we propose an approach enabling collaborative computations over data encrypted in storage, selectively involving also subjects that might not be authorized for accessing the data in plaintext when it is considered economically convenient.作者: 的闡明 時(shí)間: 2025-3-29 08:32
Multi-party Private Set Operations with an External Decidernts of multi-party PSO, for the external decider setting. All parties except the decider have a private set. Parties other than the decider neither learn this result, nor anything else from this protocol. Moreover, we studied generic solutions to the problem of PSO in the presence of an external decider.作者: 語言學(xué) 時(shí)間: 2025-3-29 13:57
Deep Learning for Detecting Network Attacks: An End-to-End Approachaws within the network protocols. Our findings show that fuzzing generates data samples that cover real-world data and deep learning models trained with fuzzed data can successfully detect real network attacks.作者: noxious 時(shí)間: 2025-3-29 16:35 作者: 引起 時(shí)間: 2025-3-29 21:45
0302-9743 ec 2021, held in Calgary, Canada, in July 2021.*.The 15 full papers and 8 short papers presented were carefully reviewed and selected from 45 submissions. The papers present high-quality original research from academia, industry, and government on theoretical and practical aspects of information sec作者: GRACE 時(shí)間: 2025-3-30 00:13
Two Quantum Cluster Approximationsntial privacy formulates an upper bound, our experiments with several datasets show that the privacy-accuracy trade-off is similar for both types of mechanisms despite the large difference in their upper bound. This suggests that the upper bound is far from the practical susceptibility to membership作者: Interstellar 時(shí)間: 2025-3-30 04:49 作者: champaign 時(shí)間: 2025-3-30 10:03 作者: frozen-shoulder 時(shí)間: 2025-3-30 13:55
Conference proceedings 20041st editioner. In order to guarantee the security and privacy of the scheme and support the multi-client model (i.e. synchronization between users), we exploit the functionality offered by AMD’s Secure Encrypted Virtualization (SEV).作者: nettle 時(shí)間: 2025-3-30 20:36
Conference proceedings 20061st editionstence of symmetric encryption schemes. We evaluate the accuracy of our classifier and show that it is significantly better than the random classifier and not much worse than more powerful classifiers (e.g., .-nearest neighbor) for which however no efficient obfuscator is known.作者: FLIRT 時(shí)間: 2025-3-30 20:42
D. P. Landau,S. P. Lewis,H. -B. Schüttlerser story contains in a named entity recognition task. Finally, it determines the type of access between the identified actors, data objects, and operations through a classification prediction. This information can then be used to construct access control documentation and information useful to stak作者: GAVEL 時(shí)間: 2025-3-31 02:57
