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Partition Based Perturbation for Privacy Preserving Distributed Data Mining
2017
Cybernetics and Information Technologies
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The perturbation maintains the statistical relationship among attributes. ...
Data mining on vertically or horizontally partitioned dataset has the overhead of protecting the private data. Perturbation is a technique that protects the revealing of data. ...
The proposed kd-anonymous perturbation method gives a smaller disclosure risk than the kd-tree method. This is shown in Fig. 2 . ...
doi:10.1515/cait-2017-0015
fatcat:7ggmcyvv6zespm2kypb4e3hy44
A Quantitative Approach for Evaluating the Utility of a Differentially Private Behavioral Science Dataset
2014
2014 IEEE International Conference on Healthcare Informatics
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Our results align with the theory of differential privacy and show when the number of records in the database is sufficiently larger than the number of cells covered by a database query, the number of ...
Objective: Social scientists who collect large amounts of medical data value the privacy of their survey participants. ...
The researcher needs to consider all of the potential risks including whether "any disclosure of the subjects' responses could reasonably place the subjects at risk of criminal or civil liability or be ...
doi:10.1109/ichi.2014.45
dblp:conf/ichi/HillHJSHX14
fatcat:dkkk7cpfjfdtrow2h6qlddghju
Non-Interactive Differential Privacy: a Survey
[article]
2012
arXiv
pre-print
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Differential privacy stands out as a model that provides strong formal guarantees about the anonymity of the participants in a sanitized database. ...
Theory, utility and a data-aware comparison are discussed on a variety of principles and concrete applications. ...
Since we are addressing the problem of statistical disclosure at large, we use R to denote a wide range of output possibilities for the mechanism designers, whose goal is to devise a mechanism function ...
arXiv:1205.2726v1
fatcat:shps24vi2jg3fibknsmltsziky
Non-interactive differential privacy
2012
Proceedings of the First International Workshop on Open Data - WOD '12
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Differential privacy stands out as a model that provides strong formal guarantees about the anonymity of the participants in a sanitized database. ...
Theory, utility and a data-aware comparison are discussed on a variety of principles and concrete applications. ...
Since we are addressing the problem of statistical disclosure at large, we use R to denote a wide range of output possibilities for the mechanism designers, whose goal is to devise a mechanism function ...
doi:10.1145/2422604.2422611
dblp:conf/wod/Leoni12
fatcat:tdfnvwiqybe7pos2z4sc4unk7q
HTF: Homogeneous Tree Framework for Differentially-Private Release of Location Data
[article]
2021
arXiv
pre-print
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We show through extensive experiments on large-scale real-world data that the proposed approach achieves superior accuracy compared to existing approaches. ...
We identify density homogeneity as a main factor driving the accuracy of DP-compliant histograms, and we build a data structure that splits the space such that data density is homogeneous within each resulting ...
Note that since the grid is fixed, enumerating split candidates as cell coordinates is data-independent, hence does not incur disclosure risk. ...
arXiv:2107.13749v1
fatcat:fpgfyky3ibgd3jcpjq6oqmk6ke
Developing privacy solutions for sharing and analysing healthcare data
2013
International Journal of Business Information Systems
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A pilot evaluation study on large real-world healthcare data shows the effectiveness of our solution in privacy protection. ...
Dataset-level properties and statistics remain approximately the same after data masking; however, individual record-level values are altered to prevent privacy disclosure. ...
The content is solely the responsibility of the authors and does not necessarily represent the official views of NSF, NLM or NIH.
Dr. Xiaobai Li ...
doi:10.1504/ijbis.2013.054335
pmid:24285983
pmcid:PMC3839961
fatcat:5nuv37m77ff4ndjxgedsixybie
Secure multidimensional range queries over outsourced data
2011
The VLDB journal
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We also study the trade-off characteristics through an extensive set of experiments on real and synthetic data. ...
For a given bucketization scheme, we derive cost and disclosure-risk metrics that estimate client's computational overhead and disclosure risk respectively. ...
Acknowledgments This work was made possible by grant from NSF (award number 1045296) and gift from NEC laboratories USA. ...
doi:10.1007/s00778-011-0245-7
fatcat:4ma6bbcxafg3jpitwtfu47ho6e
A retrospective study on the relationship between annual airborne pollen levels during four decades of 1975–2014 and annual occurrence of ulcerative colitis, Crohn's disease, primary biliary cirrhosis, fulminant hepatitis, severe acute pancreatitis, interstitial pneumonia, amyloidosis, based on the national registry database of specific intractable diseases in Japan
2019
Archives of Epidemiology and Public Health
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Koichi Iwata for his elaborate and diligent graphing work and correlation analysis.
Conflict of interest The authors declare no conflict of interest in the preparation of this article. ...
The authors have never had an opportunity of receiving any research fund in relation to this research. ...
Japanese cedar trees were rapidly cut on a large scale in the 1940s-50s, because of military and industrial needs during and after the World War II. ...
doi:10.15761/aeph.1000112
fatcat:c7n4iv55gvcc7hgx4noewbrkqy
Efficiency Analysis of Machine Learning Intelligent Investment Based on K-means Algorithm
2020
IEEE Access
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The results show that Capricorn has a certain randomness in the selection process of the fund, and chooses to reduce the rate of return in order to control the risk. ...
the advent of the era of large asset management in the domestic wealth management industry, in order to improve the efficiency of financial services, traditional finance is needed. ...
The kd tree is a binary tree that represents a division of the k-dimensional space. ...
doi:10.1109/access.2020.3011366
fatcat:asn3zm6egrfs3fsb3n7xwmwj5u
Class-Restricted Clustering and Microperturbation for Data Privacy
2013
Management science
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To accomplish this, the clustering method, which is based on a minimum spanning tree (MST) technique, uses two risk-utility tradeoff measures in the growing and pruning stages of the MST technique respectively ...
their inability to preserve important statistical properties such as the variance of attributes and the covariance across attributes. ...
The content is solely the responsibility of the authors and does not necessarily represent the official views of NLM or NIH. ...
doi:10.1287/mnsc.1120.1584
pmid:24307745
pmcid:PMC3846357
fatcat:tlehfdvraje3ng3iiglxkxqfnm
Data Clustering and Micro-perturbation for Privacy-Preserving Data Sharing and Analysis
2010
International Conference on Information Systems
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Our approach is novel in that it (i) incorporates an entropy-based measure, which represents the disclosure risk of the categorical confidential attribute, into the traditional distance measure used for ...
For numeric data, these approaches are also unable to preserve important statistical properties such as variance and covariance of the data. ...
The proposed approach adopts a minimum spanning tree (MST) technique for clustering data and takes into account the disclosure risk of the categorical confidential attribute when forming data groups. ...
dblp:conf/icis/LiS10
fatcat:wfyeketugzdghkt4frow43n2ry
Protecting Privacy When Sharing and Releasing Data with Multiple Records per Person
2020
Journal of the AIS
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This study concerns the risks of privacy disclosure when sharing and releasing a dataset in which each individual may be associated with multiple records. ...
We propose two novel measures of privacy disclosure to arrive at a more appropriate assessment of disclosure risks. ...
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Library of Medicine or the National Institutes of Health. ...
doi:10.17705/1jais.00643
fatcat:ayyymlnjejfsroqrfohxvbx3xa
Differential Privacy in Practice
2013
Journal of Computing Science and Engineering
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We briefly review the problem of statistical disclosure control under differential privacy model, which entails a formal and ad omnia privacy guarantee separating the utility of the database and the risk ...
It has born fruitful results over the past ten years, both in theoretical connections to other fields and in practical applications to real-life datasets. ...
ACKNOWLEDGMENTS This research was supported by World Class University program funded by the Ministry of Education, Science and Technology through the National Research Foundation of Korea (R31-10100). ...
doi:10.5626/jcse.2013.7.3.177
fatcat:xrcbyxpzfvh2tfdrr5572rdnku
Differentially Private Location Recommendations in Geosocial Networks
2014
2014 IEEE 15th International Conference on Mobile Data Management
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Extensive experimental results on real-world datasets show that a careful choice of differential privacy technique leads to satisfactory location recommendation results. ...
With the help of location recommendations, users are able to learn about events, products or places of interest that are relevant to their preferences. ...
): Consider a statistical database that produces the transcript U on the set of queries Q = {Q 1 , . . . , Q q }, and let ϵ > 0 be an arbitrarily-small real constant. ...
doi:10.1109/mdm.2014.13
dblp:conf/mdm/ZhangGC14
fatcat:6jdwjw6sizdtzd6ilqqwao32dq
Review on a Privacy-Preserving and Efficient kNearest Neighbor Model Based on k- Dimension Tree for Outsource Data
2021
International journal of innovations in engineering and science
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tree encryption (defined by kd-tree enhancement). traditional KNN algorithm. ...
However, because of the major security risks arising from computer computing, many organizations now encrypt data before extracting data. ...
As mentioned earlier, a reliable cloudbut-curious cloud server cannot detect any information about its use in the kd tree process and distance comparisons. ...
doi:10.46335/ijies.2021.6.8.3
fatcat:iudir6vyqbcybg3coedqej5jde
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