乐胖代购免代理版

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Asp. Comput."],"published-print":{"date-parts":[[2019,4]]},"abstract":"Abstract<\/jats:title>Analysis of a probabilistic system often requires to learn the joint probability distribution of its random variables. The computation of the exact distribution is usually an exhaustiveprecise analysis<\/jats:italic>on all executions of the system. To avoid the high computational cost of such an exhaustive search,statistical analysis<\/jats:italic>has been studied to efficiently obtain approximate estimates by analyzing only a small but representative subset of the system\u2019s behavior. In this paper we propose ahybrid statistical estimation method<\/jats:italic>that combines precise and statistical analyses to estimate mutual information, Shannon entropy, and conditional entropy, together with their confidence intervals. We show how to combine the analyses on different components of a discrete system with different accuracy to obtain an estimate for the whole system. The new method performs weighted statistical analysis with different sample sizes over different components and dynamically finds their optimal sample sizes. Moreover, it can reduce sample sizes by using prior knowledge about systems and a newabstraction-then-sampling<\/jats:italic>technique based on qualitative analysis. To apply the method to the source code of a system, we show how to decompose the code into components and to determine the analysis method for each component by overviewing the implementation of those techniques in the HyLeak tool. We demonstrate with case studies that the new method outperforms the state of the art in quantifying information leakage.<\/jats:p>","DOI":"10.1007\/s00165-018-0469-z","type":"journal-article","created":{"date-parts":[[2018,10,8]],"date-time":"2018-10-08T07:18:12Z","timestamp":1538983092000},"page":"165-206","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Hybrid statistical estimation of mutual information and its application to information flow"],"prefix":"10.1145","volume":"31","author":[{"given":"Fabrizio","family":"Biondi","sequence":"first","affiliation":[{"name":"CentraleSup\u00e9lec Rennes, Rennes, France"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2151-9560","authenticated-orcid":false,"given":"Yusuke","family":"Kawamoto","sequence":"additional","affiliation":[{"name":"AIST, Tsukuba, Japan"}]},{"given":"Axel","family":"Legay","sequence":"additional","affiliation":[{"name":"Inria, Rennes, France"}]},{"given":"Louis-Marie","family":"Traonouez","sequence":"additional","affiliation":[{"name":"Inria, Rennes, France"}]}],"member":"320","reference":[{"key":"e_1_2_1_2_1_2","doi-asserted-by":"crossref","unstructured":"Alvim MS Chatzikokolakis K Kawamoto Y Palamidessi C (2017) Information leakage games. 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