2018 IEEE Symposium on Privacy-Aware Computing (PAC)最新文献_第2页

Backward Thinking of Routing with High Uncertainties: Causal Entropy Based Routing in Multi-Agent Networks 高不确定性路由的逆向思考:多智能体网络中基于因果熵的路由

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/PAC.2018.00021

Zhonghu Xu, Kai Xing, Xuefeng Liu, Jiannong Cao

{"title":"Backward Thinking of Routing with High Uncertainties: Causal Entropy Based Routing in Multi-Agent Networks","authors":"Zhonghu Xu, Kai Xing, Xuefeng Liu, Jiannong Cao","doi":"10.1109/PAC.2018.00021","DOIUrl":"https://doi.org/10.1109/PAC.2018.00021","url":null,"abstract":"This paper is motivated by the task of modeling routing decisions with sequential forwarding interactions in multi-agent networks with topology uncertainties, e.g., agents' mobility traces with uncertain speed and direction, links to someone unknown in stranger social networks, both making their interactions come across at uncertain time and location. Since most routing designs assume that agents' behaviors be regular or with known probability distribution and envision the future of topology as stable/predictable (namely limited uncertainty), these approaches may suffer the difficulty dealing with the networks with high uncertainties. The proposed research aims to provide an effective solution for message routing among agents in such networks. Specifically, we introduce a new principle of causal entropy force in multi-agent networks for routing with high uncertainties, provide a new thinking way of routing, backward thinking, and build connections between individual intelligence, topology uncertainties, and message routing through path entropy in phase space. The experiment results with real dataset (30K taxies) indicate that the proposed method could achieve 83% message delivery rate, compared with 20%-25% of traditional approaches, and generally achieve much less latency compared with typical methods.","PeriodicalId":208309,"journal":{"name":"2018 IEEE Symposium on Privacy-Aware Computing (PAC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121332602","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: 1

[Copyright notice] (版权)

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/pac.2018.00003

引用次数: 0

Privacy-Preserving POI Recommendation Using Nonnegative Matrix Factorization 基于非负矩阵分解的隐私保护POI推荐

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/PAC.2018.00018

Xiwei Wang, Hao Yang, Kiho Lim

引用次数: 8

PAC 2018 Organizing Committee PAC 2018组委会

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/pac.2018.00006

引用次数: 0

Privacy, Polarization, and Passage of Divisive Laws 隐私、两极分化和分裂性法律的通过

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/PAC.2018.00007

Benjamin Johnson, Paul Laskowski

引用次数: 0

Privacy Enabled Crowdsourced Transmitter Localization Using Adjusted Measurements 隐私启用众包发射机定位使用调整的测量

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/PAC.2018.00016

Harsimran Singh, Shamik Sarkar, Anuj Dimri, Aditya Bhaskara, Neal Patwari, S. Kasera, Samuel Ramirez, K. Derr

{"title":"Privacy Enabled Crowdsourced Transmitter Localization Using Adjusted Measurements","authors":"Harsimran Singh, Shamik Sarkar, Anuj Dimri, Aditya Bhaskara, Neal Patwari, S. Kasera, Samuel Ramirez, K. Derr","doi":"10.1109/PAC.2018.00016","DOIUrl":"https://doi.org/10.1109/PAC.2018.00016","url":null,"abstract":"We address the problem of location privacy in the context of crowdsourced localization of spectrum offenders where participating receivers report received signal strength (RSS) measurements and their location to a central controller. We present a novel approach, that we call the adjusted measurement approach, in which we generate pseudo-locations for participating receivers and report these pseudo-locations along with adjusted RSS measurements as if the measurements were made at the pseudo-locations. The RSS values are adjusted by representing those as a weighted linear combination of the RSS values at the receivers, where receivers closer to the false location have a higher weight than those far away. We use two RSS datasets, one from a cluttered office (indoor) and another from roadways in Phoenix, Arizona (outdoor) to evaluate our approach. We compare the localization error of our approach with that of the naive approach that simply adds noise to locations. Our results demonstrate that location privacy can be preserved without a significant increase in the localization error. We also formulate an adversary attack that attempts to solve the inverse problem of determining the true locations of the receivers from their false locations. Our evaluations show that the adversary does no better than random guessing of true locations in the monitored area.","PeriodicalId":208309,"journal":{"name":"2018 IEEE Symposium on Privacy-Aware Computing (PAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128411644","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: 6

Privacy-Preserving Data Collection in Context-Aware Applications 上下文感知应用中保护隐私的数据收集

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/PAC.2018.00014

Wei Li, Chun-qiang Hu, Tianyi Song, Jiguo Yu, Xiaoshuang Xing, Zhipeng Cai

引用次数: 3

Epsilon Voting: Mechanism Design for Parameter Selection in Differential Privacy Epsilon投票:差分隐私参数选择机制设计

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/PAC.2018.00009

Nitin Kohli, Paul Laskowski

引用次数: 27

A Reputation Management Framework for MANETs manet的声誉管理框架

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/PAC.2018.00019

Shiwen Wang, Hui Xia

引用次数: 3

BigBing: Privacy-Preserving Cloud-Based Malware Classification Service BigBing:隐私保护云恶意软件分类服务

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/PAC.2018.00011

Y. Kucuk, Nikhil Patil, Zhan Shu, Guanhua Yan

{"title":"BigBing: Privacy-Preserving Cloud-Based Malware Classification Service","authors":"Y. Kucuk, Nikhil Patil, Zhan Shu, Guanhua Yan","doi":"10.1109/PAC.2018.00011","DOIUrl":"https://doi.org/10.1109/PAC.2018.00011","url":null,"abstract":"Although cloud-based malware defense services have made significant contributions to thwarting malware attacks, there have been privacy concern over using these services to analyze suspicious files which may contain user-sensitive data. We develop a new platform called BigBing (a big data approach to binary code genomics) to offer a privacy-preserving cloud-based malware classification service. BigBing relies on a community of contributors who would like to share their binary executables, and uses a novel blockchain-based scheme to ensure the privacy of possibly user-sensitive data contained within these files. To scale up malware defense services, BigBing trains user-specific classification models to detect malware attacks seen in their environments. We have implemented a prototype of BigBing, comprised of a big data cluster, a pool of servers for feature extraction, and a frontend gateway that facilitates the interaction between users and the BigBing backend. Using a real-world malware dataset, we evaluate both execution and classification performances of the service offered by BigBing. Our experimental results demonstrate that BigBing offers a useful privacy-preserving cloud-based malware classification service to fight against the ever-growing malware attacks.","PeriodicalId":208309,"journal":{"name":"2018 IEEE Symposium on Privacy-Aware Computing (PAC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126838066","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: 6