2018 IEEE Symposium on Privacy-Aware Computing (PAC)最新文献

A Cloud-Based Secure and Privacy-Preserving Clustering Analysis of Infectious Disease 基于云的传染病安全和隐私保护聚类分析

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/PAC.2018.00017

Jianqing Liu, Yaodan Hu, Hao Yue, Yanmin Gong, Yuguang Fang

{"title":"A Cloud-Based Secure and Privacy-Preserving Clustering Analysis of Infectious Disease","authors":"Jianqing Liu, Yaodan Hu, Hao Yue, Yanmin Gong, Yuguang Fang","doi":"10.1109/PAC.2018.00017","DOIUrl":"https://doi.org/10.1109/PAC.2018.00017","url":null,"abstract":"The early detection of where and when fatal infectious diseases outbreak is of critical importance to the public health. To effectively detect, analyze and then intervene the spread of diseases, people's health status along with their location information should be timely collected. However, the conventional practices are via surveys or field health workers, which are highly costly and pose serious privacy threats to participants. In this paper, we for the first time propose to exploit the ubiquitous cloud services to collect users' multi-dimensional data in a secure and privacy-preserving manner and to enable the analysis of infectious disease. Specifically, we target at the spatial clustering analysis using Kulldorf scan statistic and propose a key-oblivious inner product encryption (KOIPE) mechanism to ensure that the untrusted entity only obtains the statistic instead of individual's data. Furthermore, we design an anonymous and sybil-resilient approach to protect the data collection process from double registration attacks and meanwhile preserve participant's privacy against untrusted cloud servers. A rigorous and comprehensive security analysis is given to validate our design, and we also conduct extensive simulations based on real-life datasets to demonstrate the performance of our scheme in terms of communication and computing overhead.","PeriodicalId":208309,"journal":{"name":"2018 IEEE Symposium on Privacy-Aware Computing (PAC)","volume":"12 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":"129825820","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: 4

[Publisher's information] (发布者的信息)

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/pac.2018.00024

引用次数: 0

Deriving Privacy Settings for Location Sharing: Are Context Factors Always the Best Choice? 导出位置共享的隐私设置:上下文因素总是最好的选择吗?

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/PAC.2018.00015

Frederic Raber, A. Krüger

引用次数: 8

Privacy Preserving Scheme for Location Based Services Using Cryptographic Approach 基于位置服务的加密隐私保护方案

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/PAC.2018.00022

Yuwen Pu, Jinbiao Luo, Ying Wang, Chun-qiang Hu, Yan Huo, Jiong Zhang

引用次数: 9

Informational Privacy, A Right to Explanation, and Interpretable AI 信息隐私、解释权和可解释的人工智能

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/PAC.2018.00013

Tae Wan Kim, Bryan R. Routledge

引用次数: 20

Malware Variants Detection Using Behavior Destructive Features 使用行为破坏性功能的恶意软件变体检测

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/PAC.2018.00020

Yongle Chen, Bingchu Jin, Dan Yu, Junjie Chen

引用次数: 2

Message from the PAC 2018 Chairs 2018年PAC主席的致辞

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/PAC.2018.00005

Xiuzhen Cheng, J. Valacich, N. Zhang

引用次数: 0

[Title page i] [标题页i]

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/pac.2018.00001

引用次数: 0

PhotoSafer: Content-Based and Context-Aware Private Photo Protection for Smartphones PhotoSafer:基于内容和上下文感知的智能手机私人照片保护

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/PAC.2018.00008

Ang Li, David Darling, Qinghua Li

{"title":"PhotoSafer: Content-Based and Context-Aware Private Photo Protection for Smartphones","authors":"Ang Li, David Darling, Qinghua Li","doi":"10.1109/PAC.2018.00008","DOIUrl":"https://doi.org/10.1109/PAC.2018.00008","url":null,"abstract":"Nowadays many people store photos in smartphones. Many of the photos contain sensitive, private information, such as a photocopy of driver's license and credit card. An arising privacy concern is with the unauthorized accesses to such private photos by installed apps. The Android permission system offers all-or-nothing access to photos stored on smartphones, which is still coarse-grained control and makes users unaware of the exact behavior of installed apps. Our analysis found that 82% of the top 200 free apps have complete access to stored photos and network on a user's smartphone. In addition, our user survey revealed that 87.5% of 112 respondents are not aware that certain apps can access their photos without informing users, and all the respondents believe that the stored photos on their smartphones contain different types of private information. Hence, we propose PhotoSafer, a content-based, context-aware private photo protection system for Android phones. PhotoSafer can detect private photos based on photo content with a well-trained deep convolutional neural network, and control access to photos based on system status (e.g., screen is locked) and app running status (e.g., background). Evaluations demonstrate that PhotoSafer can accurately identify private photos in real time. The effectiveness and efficiency of the implemented prototype system show the potential for practical use.","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":"124934382","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: 5

Efficient Privacy-Preserving Outsourcing of Large-Scale Geometric Programming 大规模几何规划的高效隐私保护外包

2018 IEEE Symposium on Privacy-Aware Computing (PAC) Pub Date : 2018-09-01 DOI: 10.1109/PAC.2018.00012

Wei Bao, Qinghua Li

{"title":"Efficient Privacy-Preserving Outsourcing of Large-Scale Geometric Programming","authors":"Wei Bao, Qinghua Li","doi":"10.1109/PAC.2018.00012","DOIUrl":"https://doi.org/10.1109/PAC.2018.00012","url":null,"abstract":"Nowadays industries are collecting a massive and exponentially growing amount of data that can potentially promote business innovations. However, it is challenging for resourcelimited clients to analyze their data in a cost-effective and timely way as the data volume keeps growing. With cloud computing, one feasible solution is to analyze the massive data by outsourcing them to the cloud. Nonetheless, clients’ data may contain private information that needs to be kept secret. In this paper, we design a secure, efficient, and verifiable outsourcing protocol specifically for geometric programming, which is one of the most fundamental problems in data analysis with many applications. In particular, a secure and efficient transformation scheme is used to encrypt the original geometric programming problem at the client side and protect its privacy before offloading it, and the gradient projection method is employed to solve the encrypted geometric programming problem in the cloud side. Experiments are conducted on both Amazon Elastic Compute Cloud (EC2) and a laptop to evaluate performance of the designed outsourcing protocol, and the results show the feasibility and efficiency of the protocol.","PeriodicalId":208309,"journal":{"name":"2018 IEEE Symposium on Privacy-Aware Computing (PAC)","volume":"8 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":"124802333","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: 3