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Algorithm for Hiding High Utility Sensitive Association Rule Based on Intersection Lattice 基于交格的高效用敏感关联规则隐藏算法
International Conference on Multimedia Analysis and Pattern Recognition Pub Date : 2018-04-05 DOI: 10.1109/MAPR.2018.8337512
V. Trieu, Chau Truong Ngoc, H. L. Quoc, N. N. Si
{"title":"Algorithm for Hiding High Utility Sensitive Association Rule Based on Intersection Lattice","authors":"V. Trieu, Chau Truong Ngoc, H. L. Quoc, N. N. Si","doi":"10.1109/MAPR.2018.8337512","DOIUrl":"https://doi.org/10.1109/MAPR.2018.8337512","url":null,"abstract":"Hiding high utility sensitive association rule is an essential problem for preserving privacy knowledge from being revealed while sharing data outside the parties. However, this problem has not been considered thoughtfully. This paper aims to propose a novel strategy for hiding high utility sensitive association rules based on intersection lattice. The strategy includes two steps: (i) The transactions containing the sensitive rule and having the least utility are selected as victim transactions; (ii) The victim items are specified based on a heuristic in such a way that modifying them causes the least impact on lattice of high transaction weighted utility itemsets. Relying on those steps, the algorithm named HHUARL for hiding high utility sensitive association rules is proposed. The expriment shows that side effects caused by HHUARL algorithm is acceptable.","PeriodicalId":117354,"journal":{"name":"International Conference on Multimedia Analysis and Pattern Recognition","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121682971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multivariate Filter for Saliency 多元显著性滤波
International Conference on Multimedia Analysis and Pattern Recognition Pub Date : 2018-04-01 DOI: 10.1109/MAPR.2018.8337522
Dao Nam Anh
{"title":"Multivariate Filter for Saliency","authors":"Dao Nam Anh","doi":"10.1109/MAPR.2018.8337522","DOIUrl":"https://doi.org/10.1109/MAPR.2018.8337522","url":null,"abstract":"A method for analytical computing of finding objects of interest in images has been developed with multivariate normal distribution to improve the visual capability. Key requirement is the ability of significantly shifting attention to image region that is texture based in general case of real images. The visual attention evaluation is mainly involved for initial task of most visual applications including segmentation, gaze tracking and image re-targeting. To enhance the accuracy of saliency detection, we have to analyze the salient distinction of textured region by combining several techniques. As an initial step, the multivariate filters are designed for estimating local texture feature that is rotation invariant. Significant distinction of patches is then calculated to describe the possible interest regions. The final morphological operations bring fixation of objects of interest. On a test set which consists of ten thousands of images in several themes, the method provides a precision of 92%, recall of 83% and F-measure of 86%.","PeriodicalId":117354,"journal":{"name":"International Conference on Multimedia Analysis and Pattern Recognition","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125464353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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