GeoPrivacy '14最新文献

A spatial entropy-based approach to improve mobile risk-based authentication 基于空间熵的移动风险认证改进方法

GeoPrivacy '14 Pub Date : 2014-11-04 DOI: 10.1145/2675682.2676400

J. Xiong, J. Xiong, Christophe Claramunt

{"title":"A spatial entropy-based approach to improve mobile risk-based authentication","authors":"J. Xiong, J. Xiong, Christophe Claramunt","doi":"10.1145/2675682.2676400","DOIUrl":"https://doi.org/10.1145/2675682.2676400","url":null,"abstract":"The research presented in this paper develops a novel approach for a risk-based authentication system that takes into account mobile user movement patterns. Inspired by the concept of Shannon's information theory, we introduce a variant version of spatial entropy vectors embedded with time information as a mathematical modeling tool to evaluate regular movement patterns, and spatial entropy vectors derived from user movements range and paces. To support the approach, several algorithms have been designed and implemented. A prototype iPhone application was developed as a proof-of-concept, user movement data has been collected over a predetermined timeframe by accumulating, merging, and saving spatial entropy vectors in a database on the application. The application simulates risk-based authentication by calculating risk factors based on the similarity between current spatial entropy vectors calculated on demand, and historical distributions of movement patterns. Data collected on the field shows that the risk factor is relatively low for similar moving patterns, while different patterns can yield a higher risk factor. Rather than modeling this process by directly storing GPS location data with complicated path-matching algorithms, the spatial entropy model developed uses sampled location data, but does not keep it, preserving user privacy. Practical applications can be used, for example, to adjust fingerprint authentication threshold in iPhone when combining with the risk factor calculated in real time.","PeriodicalId":337858,"journal":{"name":"GeoPrivacy '14","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124315520","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: 8

A grid-based location privacy-preserving method for LBS users 基于网格的LBS用户位置隐私保护方法

GeoPrivacy '14 Pub Date : 2014-11-04 DOI: 10.1145/2675682.2676398

Lei Mou, A. Lbath

引用次数: 1

Quantity based aggregation for cadastral databases 基于数量的地籍数据库聚合

GeoPrivacy '14 Pub Date : 2014-11-04 DOI: 10.1145/2675682.2676394

Firas Al Khalil, A. Gabillon, P. Capolsini

引用次数: 3

A civilized cyberspace for geoprivacy 文明的地理隐私网络空间

GeoPrivacy '14 Pub Date : 2014-11-04 DOI: 10.1145/2675682.2676396

Paul Weiser, S. Scheider

引用次数: 13

SUDO: a secure database outsourcing solution for location-based systems SUDO:用于基于位置的系统的安全数据库外包解决方案

GeoPrivacy '14 Pub Date : 2014-11-04 DOI: 10.1145/2675682.2676397

Ling-Jyh Chen, Chen-Ruei Hong, D. Deng, Hu-Cheng Lee, Hsin-Hung Hsieh

引用次数: 1

Protecting patient geo-privacy via a triangular displacement geo-masking method 通过三角位移地理掩蔽法保护患者地理隐私

GeoPrivacy '14 Pub Date : 2014-11-04 DOI: 10.1145/2675682.2676399

Abdullah Murad, Brian N. Hilton, T. Horan, John Tangenberg

{"title":"Protecting patient geo-privacy via a triangular displacement geo-masking method","authors":"Abdullah Murad, Brian N. Hilton, T. Horan, John Tangenberg","doi":"10.1145/2675682.2676399","DOIUrl":"https://doi.org/10.1145/2675682.2676399","url":null,"abstract":"Protecting patient geo-privacy while allowing for valid geographic analyses of the data is a major challenge [1]. As a consequence, a variety of methods have been introduced to mask patients' locational information, also called geo-masking methods [2]. This study assessed the five main geo-masking methods as cited by [3] in terms of re-identification risk and performance. These five methods are Random Direction and Fixed Radius, Random Perturbation within a Circle, Gaussian Displacement, Donut Masking, and Bimodal Gaussian Displacement. Based on the assessment, the study highlighted two major gaps in the design of these geo-masking methods. First, all five geo-masking methods used only population density in calculating the displacement distances between the original locations of points and their new locations. However, other criteria that might be as important as population density were not considered in designing these five methods. These include data sensitivity, research types, quasi-indicator availability, previously generated maps availability, end-users' types, and the possibility of temporal synergy of data. Second, the Donut Masking and the Bimodal Gaussian Displacement methods were found to be superior in terms of minimizing re-identifying risks, but they were also found to be consuming much more processing power compared to the other three geo-masking methods. To address these gaps, this study proposed a new geo-masking method, called the \"Triangular Displacement\". The primary design, development, and evaluation of the Triangular Displacement method were based on the Design Science Research (DSR) Process Model [4], also known as DSRM. The expected next step is to implement the resultant geo-masking method as a tool to help healthcare data guardians de-identify large sets of PHR automatically. A pilot study with a large Southern Californian healthcare provider has been outlined to examine the efficacy of the developed solution.","PeriodicalId":337858,"journal":{"name":"GeoPrivacy '14","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114653240","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: 8

Venue attacks in location-based social networks 基于位置的社交网络中的地点攻击

GeoPrivacy '14 Pub Date : 2014-11-04 DOI: 10.1145/2675682.2676395

Lei Jin, Hassan Takabi

引用次数: 1