Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics最新文献_第2页

Session details: Research Paper Session 2 会议详情:研究论文会议2

Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics Pub Date : 2016-03-11 DOI: 10.1145/3255950

E. Leiss

引用次数: 0

Static Analysis of Malicious Java Applets 恶意Java小程序的静态分析

Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics Pub Date : 2016-03-11 DOI: 10.1145/2875475.2875477

N. Ganesh, Fabio Di Troia, C. A. Visaggio, Thomas H. Austin, M. Stamp

引用次数: 12

Differential Privacy for Collaborative Filtering Recommender Algorithm 协同过滤推荐算法的差分隐私

Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics Pub Date : 2016-03-11 DOI: 10.1145/2875475.2875483

Xue Zhu, Yuqing Sun

{"title":"Differential Privacy for Collaborative Filtering Recommender Algorithm","authors":"Xue Zhu, Yuqing Sun","doi":"10.1145/2875475.2875483","DOIUrl":"https://doi.org/10.1145/2875475.2875483","url":null,"abstract":"Collaborative filtering plays an essential role in a recommender system, which recommends a list of items to a user by learning behavior patterns from user rating matrix. However, if an attacker has some auxiliary knowledge about a user purchase history, he/she can infer more information about this user. This brings great threats to user privacy. Some methods adopt differential privacy algorithms in collaborative filtering by adding noises to a rating matrix. Although they provide theoretically private results, the influence on recommendation accuracy are not discussed. In this paper, we solve the privacy problem in recommender system in a different way by applying the differential privacy method into the procedure of recommendation. We design two differentially private recommender algorithms with sampling, named Differentially Private Item Based Recommendation with sampling (DP-IR for short) and Differentially Private User Based Recommendation with sampling(DP-UR for short). Both algorithms are based on the exponential mechanism with a carefully designed quality function. Theoretical analyses on privacy of these algorithms are presented. We also investigate the accuracy of the proposed method and give theoretical results. Experiments are performed on real datasets to verify our methods.","PeriodicalId":393015,"journal":{"name":"Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116605541","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: 24

Session details: Research Paper Session 1 会议详情:研究论文会议1

Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics Pub Date : 2016-03-11 DOI: 10.1145/3255946

Stephen S. H. Huang

引用次数: 0

How Can We Enable Privacy in an Age of Big Data Analytics? 在大数据分析时代，我们如何保护隐私?

Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics Pub Date : 2016-03-11 DOI: 10.1145/2875475.2875489

C. Landwehr

{"title":"How Can We Enable Privacy in an Age of Big Data Analytics?","authors":"C. Landwehr","doi":"10.1145/2875475.2875489","DOIUrl":"https://doi.org/10.1145/2875475.2875489","url":null,"abstract":"Even though some seem to think privacy is dead, we are all still wearing clothes, as Bruce Schneier observed at a recent conference on surveillance[1]. Yet big data and big data analytics are leaving some of us feeling a bit more naked than before. This talk will provide some personal observations on privacy today and then outline some research areas where progress is needed to enable society to gain the benefits of analyzing large datasets without giving up more privacy than necessary. Not since the early 1970s, when computing pioneer Willis Ware chaired the committee that produced the initial Fair Information Practice Principles [2] has privacy been so much in the U.S. public eye. Snowden's revelations, as well as a growing awareness that merely living our lives seems to generate an expanding \"digital exhaust.\" Have triggered many workshops and meetings. A national strategy for privacy research is in preparation by a Federal interagency group. The ability to analyze large datasets rapidly and to extract commercially useful insights from them is spawning new industries. Must this industrial growth come at the cost of substantial privacy intrusions?","PeriodicalId":393015,"journal":{"name":"Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126886592","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

Evaluation of Android Malware Detection Based on System Calls 基于系统调用的Android恶意软件检测评估

Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics Pub Date : 2016-03-11 DOI: 10.1145/2875475.2875487

Marko Dimjasevic, Simone Atzeni, I. Ugrina, Zvonimir Rakamaric

{"title":"Evaluation of Android Malware Detection Based on System Calls","authors":"Marko Dimjasevic, Simone Atzeni, I. Ugrina, Zvonimir Rakamaric","doi":"10.1145/2875475.2875487","DOIUrl":"https://doi.org/10.1145/2875475.2875487","url":null,"abstract":"With Android being the most widespread mobile platform, protecting it against malicious applications is essential. Android users typically install applications from large remote repositories, which provides ample opportunities for malicious newcomers. In this paper, we evaluate a few techniques for detecting malicious Android applications on a repository level. The techniques perform automatic classification based on tracking system calls while applications are executed in a sandbox environment. We implemented the techniques in the maline tool, and performed extensive empirical evaluation on a suite of around 12,000 applications. The evaluation considers the size and type of inputs used in analyses. We show that simple and relatively small inputs result in an overall detection accuracy of 93% with a 5% benign application classification error, while results are improved to a 96% detection accuracy with up-sampling. This indicates that system-call based techniques are viable to be used in practice. Finally, we show that even simplistic feature choices are effective, suggesting that more heavyweight approaches should be thoroughly (re)evaluated.","PeriodicalId":393015,"journal":{"name":"Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics","volume":"150 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132125106","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: 95

Session details: Keynote Session 会议详情:主题会议

Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics Pub Date : 2016-03-11 DOI: 10.1145/3255949

C. Ordonez

引用次数: 0

Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics 2016年ACM安全与隐私分析国际研讨会论文集

Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics Pub Date : 1900-01-01 DOI: 10.1145/2875475

引用次数: 0