scholarly journals A Review of Differential Privacy in Individual Data Release

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Jun Wang ◽  
Shubo Liu ◽  
Yongkai Li
Keyword(s):  
Pattern Mining ◽  
Medical Information ◽  
Differential Privacy ◽  
Rapid Development ◽  
Frequent Pattern ◽  
Future Research ◽  
Healthcare Applications ◽  
Tree Structures ◽  
Research Results ◽  
Data Release

The rapid development of mobile technology has improved users’ quality of treatment, and tremendous amounts of medical information are readily available and widely used in data analysis and application, which bring on serious threats to users’ privacy. Classical methods based on cryptography and anonymous-series models fail due to their high complexity, poor controllability, and dependence on the background knowledge of adversaries when it comes to current mobile healthcare applications. Differential privacy is a relatively new notion of privacy and has become the de facto standard for a security-controlled privacy guarantee. In this paper, the key aspects of basic concepts and implementation mechanisms related to differential privacy are explained, and the existing research results are concluded. The research results presented include methods based on histograms, tree structures, time series, graphs, and frequent pattern mining data release methods. Finally, shortcomings of existing methods and suggested directions for future research are presented.

scholarly journals Protecting patient privacy in survival analyses

2019 ◽  
Vol 27 (3) ◽  
pp. 366-375
Author(s):  
Luca Bonomi ◽  
Xiaoqian Jiang ◽  
Lucila Ohno-Machado
Keyword(s):  
Survival Analysis ◽  
Biomedical Research ◽  
Privacy Protection ◽  
Differential Privacy ◽  
Future Research ◽  
Patient Privacy ◽  
Privacy Risk ◽  
Survival Curves ◽  
Healthcare Applications ◽  
Survival Analyses

Abstract Objective Survival analysis is the cornerstone of many healthcare applications in which the “survival” probability (eg, time free from a certain disease, time to death) of a group of patients is computed to guide clinical decisions. It is widely used in biomedical research and healthcare applications. However, frequent sharing of exact survival curves may reveal information about the individual patients, as an adversary may infer the presence of a person of interest as a participant of a study or of a particular group. Therefore, it is imperative to develop methods to protect patient privacy in survival analysis. Materials and Methods We develop a framework based on the formal model of differential privacy, which provides provable privacy protection against a knowledgeable adversary. We show the performance of privacy-protecting solutions for the widely used Kaplan-Meier nonparametric survival model. Results We empirically evaluated the usefulness of our privacy-protecting framework and the reduced privacy risk for a popular epidemiology dataset and a synthetic dataset. Results show that our methods significantly reduce the privacy risk when compared with their nonprivate counterparts, while retaining the utility of the survival curves. Discussion The proposed framework demonstrates the feasibility of conducting privacy-protecting survival analyses. We discuss future research directions to further enhance the usefulness of our proposed solutions in biomedical research applications. Conclusion The results suggest that our proposed privacy-protection methods provide strong privacy protections while preserving the usefulness of survival analyses.

Research of Data Graph Mining Based on Telecommunication Customers

2013 ◽  
Vol 443 ◽  
pp. 402-406 ◽  
Author(s):  
Shang Gao ◽  
Mei Mei Li
Keyword(s):  
Data Mining ◽  
Graph Mining ◽  
Pattern Mining ◽  
Rapid Development ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Practical Significance ◽  
Research Progress ◽  
Graph Data ◽  
Data Graph

With the rapid development of the number of mobile phone users has accumulated a large number of graph data, graph data mining has gradually become a hot area of research. Traditional data such as clustering, classification, frequent pattern mining gradually extended to the field of graph data mining research. Introduced at this stage graph data mining technology research progress, summarizes the characteristics of the graphical data mining, practical significance, the main problem, and scenarios to discuss and forecast chart data, especially research on uncertain graph data become trends and hot spots.

scholarly journals Travel Trajectory Frequent Pattern Mining Based on Differential Privacy Protection

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Weiya Wang ◽  
Geng Yang ◽  
Lin Bao ◽  
Ke Ma ◽  
Hao Zhou ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Pattern Mining ◽  
Differential Privacy ◽  
Data Availability ◽  
Frequent Pattern ◽  
Frequent Subgraph Mining ◽  
Trajectory Data ◽  
User Privacy ◽  
Location Data ◽  
Subgraph Mining

Now, many application services based on location data have brought a lot of convenience to people’s daily life. However, publishing location data may divulge individual sensitive information. Because the location records about location data may be discrete in the database, some existing privacy protection schemes are difficult to protect location data in data mining. In this paper, we propose a travel trajectory data record privacy protection scheme (TMDP) based on differential privacy mechanism, which employs the structure of a trajectory graph model on location database and frequent subgraph mining based on weighted graph. Time series is introduced into the location data; the weighted trajectory model is designed to obtain the travel trajectory graph database. We upgrade the mining of location data to the mining of frequent trajectory graphs, which can discover the relationship of location data from the database and protect location data mined. In particular, to improve the identification efficiency of frequent trajectory graphs, we design a weighted trajectory graph support calculation algorithm based on canonical code and subgraph structure. Moreover, to improve the data utility under the premise of protecting user privacy, we propose double processes of adding noises to the subgraph mining process by the Laplace mechanism and selecting final data by the exponential mechanism. Through formal privacy analysis, we prove that our TMDP framework satisfies ε -differential privacy. Compared with the other schemes, the experiments show that the data availability of the proposed scheme is higher and the privacy protection of the scheme is effective.

Projecting a European Diploma in Psychology

2002 ◽  
Vol 7 (3) ◽  
pp. 221-224 ◽  
Author(s):  
Bernhard Wilpert
Keyword(s):  
Research Process ◽  
Project Team ◽  
Future Research ◽  
Research Results ◽  
External Constraints ◽  
Internal Procedures ◽  
The Eu

The paper presents an inside evaluation of the EuroPsyT project, funded by the EU Leonardo Program in 1999-2001. While standard research usually neglects to reflect on the internal and external constraints and opportunities under which research results are achieved, the paper stresses exactly those aspects: starting from a brief description of the overall objectives of the 11 countries project, the paper proceeds to describe the macro-context and the internal strengths and weaknesses of the project team, the internal procedures of cooperation,. and obstacles encountered during the research process. It winds up in noting some of the project's achievements and with a look towards future research.

An Adaptive Data Distribution Through Tree Rules in Frequent Pattern Mining

2018 ◽  
pp. 300-305
Keyword(s):  
Information Sharing ◽  
Pattern Mining ◽  
Data Distribution ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
General Development ◽  
Secure Information ◽  
Evaluation Parameters ◽  
Secure Information Sharing

Information sharing among the associations is a general development in a couple of zones like business headway and exhibiting. As bit of the touchy principles that ought to be kept private may be uncovered and such disclosure of delicate examples may impacts the advantages of the association that have the data. Subsequently the standards which are delicate must be secured before sharing the data. In this paper to give secure information sharing delicate guidelines are bothered first which was found by incessant example tree. Here touchy arrangement of principles are bothered by substitution. This kind of substitution diminishes the hazard and increment the utility of the dataset when contrasted with different techniques. Examination is done on certifiable dataset. Results shows that proposed work is better as appear differently in relation to various past strategies on the introduce of evaluation parameters.

Learning and Synchronized Privacy Preserving Frequent Pattern Mining

2011 ◽  
Vol 22 (8) ◽  
pp. 1749-1760
Author(s):  
Yu-Hong GUO ◽  
Yun-Hai TONG ◽  
Shi-Wei TANG ◽  
Leng-Dong WU
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Privacy Preserving ◽  
Frequent Pattern

scholarly journals Comparison of disclosure/concealment of medical information given to conversational agents or to physicians

2021 ◽  
Vol 27 (1) ◽  
pp. 146045822199486
Author(s):  
Nicholas RJ Frick ◽  
Felix Brünker ◽  
Björn Ross ◽  
Stefan Stieglitz
Keyword(s):  
Natural Language ◽  
Medical Information ◽  
Frame Of Reference ◽  
Future Research ◽  
Negative Consequences ◽  
Conversational Agents ◽  
Online Questionnaire ◽  
Computer Based

Within the anamnesis, medical information is frequently withheld, incomplete, or incorrect, potentially causing negative consequences for the patient. The use of conversational agents (CAs), computer-based systems using natural language to interact with humans, may mitigate this problem. The present research examines whether CAs differ from physicians in their ability to elicit truthful disclosure and discourage concealment of medical information. We conducted an online questionnaire with German participants ( N = 148) to assess their willingness to reveal medical information. The results indicate that patients would rather disclose medical information to a physician than to a CA; there was no difference in the tendency to conceal information. This research offers a frame of reference for future research on applying CAs during the anamnesis to support physicians. From a practical view, physicians might gain better understanding of how the use of CAs can facilitate the anamnesis.

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