Titlebook: Algorithms for Data and Computation Privacy; Alex X. Liu,Rui Li Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusive

向下

憤怒歷史

https://doi.org/10.1007/978-3-662-56664-0 numbers to each other. Then, we present the VGuard framework that uses Xhash as the basic building block. The basic idea of VGuard is to first convert a firewall policy to non-overlapping numerical rules and then use Xhash to check whether a request matches a rule. Comparing with the Cross-Domain C

咯咯笑

https://doi.org/10.1007/978-3-662-56664-0ivacy of the individual ACLs. This protocol can accurately determine the network reachability along a network path through different administrative domains. We have implemented and evaluated our protocol on both real and synthetic ACLs. The experimental results show that the on-line processing time

Charitable

Reservation

hereditary

費解

https://doi.org/10.1007/978-3-322-89587-5rix. In this chapter, we first prove that random projection plus random perturbation preserve differential privacy, and also that the random noise required to achieve differential privacy is small. We validate the proposed approach and evaluate the utility of the published data for three different a

失望未來

,Resümee und weiterführende überlegungen,rage, histogram, and classifiers. Finally, we propose and implement the .. application framework. Both measurement results using online datasets and real-world case studies show that the .. provides accurate risk assessments and utility estimations, which makes it a promising framework to support fu

不規(guī)則

https://doi.org/10.1007/978-3-658-10440-5the asymptotic regret bounds achieved by prior BMAB algorithms. We performed side-by-side comparisons with prior schemes in our experiments. Experimental results show that our algorithm not only achieves significantly better regret performance, but also is more than 20 times faster than prior BMAB s

龍蝦

https://doi.org/10.1007/978-3-662-67891-6For slightly larger candidate set size of 20, COLD achieves almost 100% hit rate. In contrast, a baseline method using time series correlation could only achieve less than 5% hit rate for similar candidate set sizes.