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Pattern-Guided k-Anonymity [PDF]
Algorithms, 2013 We suggest a user-oriented approach to combinatorial data anonymization. A data matrix is called k-anonymous if every row appears at least k times—the goal of the NP-hard k-ANONYMITY problem then is to make a given matrix k-anonymous by suppressing ...
Rolf Niedermeier +2 more
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Daily activity locations k-anonymity for the evaluation of disclosure risk of individual GPS datasets [PDF]
International Journal of Health Geographics, 2020 Background Personal privacy is a significant concern in the era of big data. In the field of health geography, personal health data are collected with geographic location information which may increase disclosure risk and threaten personal geoprivacy ...
Jue Wang, Mei-Po Kwan
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A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks. [PDF]
PLoS ONE, 2017 Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications.
Haitao Zhang +4 more
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MultiRelational k-Anonymity [PDF]
2007 IEEE 23rd International Conference on Data Engineering, 2007 k-anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous dataset, any identifying information occurs in at least k tuples. Much research has been done to modify a single table dataset to satisfy anonymity constraints.
Nergiz, M.E., Clifton, C., Nergiz, A.E.
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Nonexposure Accurate Location K-Anonymity Algorithm in LBS [PDF]
The Scientific World Journal, 2014 This paper tackles location privacy protection in current location-based services (LBS) where mobile users have to report their exact location information to an LBS provider in order to obtain their desired services.
Jinying Jia, Fengli Zhang
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k-Same-Net: k-Anonymity with Generative Deep Neural Networks for Face Deidentification [PDF]
Entropy, 2018 Image and video data are today being shared between government entities and other relevant stakeholders on a regular basis and require careful handling of the personal information contained therein. A popular approach to ensure privacy protection in such
Blaž Meden +3 more
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Location Prediction Based on Transition Probability Matrices Constructing from Sequential Rules for Spatial-Temporal K-Anonymity Dataset. [PDF]
PLoS ONE, 2016 Spatial-temporal k-anonymity has become a mainstream approach among techniques for protection of users' privacy in location-based services (LBS) applications, and has been applied to several variants such as LBS snapshot queries and continuous queries ...
Haitao Zhang +4 more
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Efficient Multidimensional Suppression for K-Anonymity [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2010 Many applications that employ data mining techniques involve mining data that include private and sensitive information about the subjects. One way to enable effective data mining while preserving privacy is to anonymize the data set that includes private information about subjects before being released for data mining. One way to anonymize data set is
Slava Kisilevich +3 more
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Optimization-Based k-Anonymity Algorithms
Computers & Security, 2020 <p>In this paper we present a formulation of <em>k</em>-anonymity as a mathematical <a href="https://www.sciencedirect.com/topics/computer-science/optimization-problem" target="_blank">optimization problem</a>. In solving this formulated problem, <em>k</em>-anonymity is achieved while maximizing the utility of ...
Yuting Liang, Reza Samavi
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网络与信息安全学报, 2023 The use of location-based service brings convenience to people’s daily lives, but it also raises concerns about users’ location privacy.In the k-nearest neighbor query problem, constructing K-anonymizing spatial regions is a method used to protects users’
Saite CHEN, Weihai LI, Yuanzhi YAO, Nenghai YU
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