MHD: A New Method towards Privacy Protecting Datasets Published

2012 ◽  
Vol 214 ◽  
pp. 792-798
Author(s):  
Fei Liu ◽  
Yan Jia ◽  
Wei Hong Han
Keyword(s):  
Information Loss ◽  
Cost Data ◽  
New Method ◽  
Time Cost ◽  
Clustering Method ◽  
Empirical Results ◽  
High Security ◽  
Sensitive Attribute

In this paper, we proposed a multi-hierarchical diversity algorithm MHD to prevent privacy disclosing in dataset. We proposed some definitions of multi-hierarchical diversity firstly. Sensitive values are partitioned into several classes. We ensured no proportion of class exceeding the threshold. We generalized some values of sensitive attribute to reduce information loss. Clustering method was used to lower data distort. Greed algorithm was used to lower time cost. We compared MHD with classic algorithms, ε-cloning and m-Invariance about Time Cost, Data Distort, Usability and Imbalance. Empirical results showed that our algorithm could protect privacy and publish datasets with high security and lower information loss

Maintaining and Updating the Storing Data by the Control Points

2013 ◽  
Vol 457-458 ◽  
pp. 793-796
Author(s):  
I. Mimorov ◽  
I. Livshits ◽  
V. Vasilev
Keyword(s):  
Good Practice ◽  
New Method ◽  
Control Points ◽  
Competitive Advantages ◽  
Distance Teaching ◽  
Time Cost ◽  
Teaching System ◽  
System Usage ◽  
Potential Risks ◽  
To Receive

This paper describes the new method that improves the processing and storing of data, which was used during the development of distance teaching system. Usage of a modern methodologies and good practice has reduced the time cost for working with information, helps to identify the out of day information, operate potential risks and shows how to receive competitive advantages.

scholarly journals A New Approach for Resolving Conflicts in Actionable Behavioral Rules

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Peng Su ◽  
Dan Zhu ◽  
Daniel Zeng
Keyword(s):  
Prediction Accuracy ◽  
New Method ◽  
Best Interest ◽  
New Approach ◽  
Actionable Knowledge ◽  
Empirical Results ◽  
Ranking Procedure ◽  
Valid Method ◽  
Weight Parameter ◽  
Behavioral Rules

Knowledge is considered actionable if users can take direct actions based on such knowledge to their advantage. Among the most important and distinctive actionable knowledge are actionable behavioral rules that can directly and explicitly suggest specific actions to take to influence (restrain or encourage) the behavior in the users’ best interest. However, in mining such rules, it often occurs that different rules may suggest the same actions with different expected utilities, which we call conflicting rules. To resolve the conflicts, a previous valid method was proposed. However, inconsistency of the measure for rule evaluating may hinder its performance. To overcome this problem, we develop a new method that utilizes rule ranking procedure as the basis for selecting the rule with the highest utility prediction accuracy. More specifically, we propose an integrative measure, which combines the measures of the support and antecedent length, to evaluate the utility prediction accuracies of conflicting rules. We also introduce a tunable weight parameter to allow the flexibility of integration. We conduct several experiments to test our proposed approach and evaluate the sensitivity of the weight parameter. Empirical results indicate that our approach outperforms those from previous research.

scholarly journals Privacy Preserving Data Publishing for Multiple Sensitive Attributes Based on Security Level

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 166
Author(s):  
Yuelei Xiao ◽  
Haiqi Li
Keyword(s):  
Data Privacy ◽  
Privacy Preserving ◽  
Information Loss ◽  
Experimental Results ◽  
Data Publishing ◽  
Security Level ◽  
Sensitive Attribute ◽  
Data Volume ◽  
Security Levels ◽  
Privacy Preserving Data Publishing

Privacy preserving data publishing has received considerable attention for publishing useful information while preserving data privacy. The existing privacy preserving data publishing methods for multiple sensitive attributes do not consider the situation that different values of a sensitive attribute may have different sensitivity requirements. To solve this problem, we defined three security levels for different sensitive attribute values that have different sensitivity requirements, and given an L s l -diversity model for multiple sensitive attributes. Following this, we proposed three specific greed algorithms based on the maximal-bucket first (MBF), maximal single-dimension-capacity first (MSDCF) and maximal multi-dimension-capacity first (MMDCF) algorithms and the maximal security-level first (MSLF) greed policy, named as MBF based on MSLF (MBF-MSLF), MSDCF based on MSLF (MSDCF-MSLF) and MMDCF based on MSLF (MMDCF-MSLF), to implement the L s l -diversity model for multiple sensitive attributes. The experimental results show that the three algorithms can greatly reduce the information loss of the published microdata, but their runtime is only a small increase, and their information loss tends to be stable with the increasing of data volume. And they can solve the problem that the information loss of MBF, MSDCF and MMDCF increases greatly with the increasing of sensitive attribute number.

Collecting Direct Non-Health Care and Time Cost Data: Application to Screening and Diagnosis of Cervical Cancer

2006 ◽  
Vol 26 (3) ◽  
pp. 265-272 ◽  
Author(s):  
Scott B. Cantor ◽  
Lawrence B. Levy ◽  
Marylou Cárdenas-Turanzas ◽  
Karen Basen-Engquist ◽  
Tao Le ◽  
...  
Keyword(s):  
Health Care ◽  
Cervical Cancer ◽  
Cost Data ◽  
Time Cost ◽  
Data Application ◽  
Screening And Diagnosis

Using fuzzy clustering to reveal recurring spatial patterns in corpora of dialect maps

2012 ◽  
Vol 17 (2) ◽  
pp. 176-197 ◽  
Author(s):  
Daniel Meschenmoser ◽  
Simon Pröll
Keyword(s):  
Spatial Patterns ◽  
Fuzzy Clustering ◽  
Statistical Properties ◽  
New Method ◽  
Clustering Method ◽  
Semantic Relationships ◽  
Fuzzy C Means ◽  
Lexical Data ◽  
Empirical Covariance

In this article, a new method to identify groups of spatially similar dialect maps is presented. This is done by comparing statistical properties of the maps: the empirical covariance is measured for every map in a corpus of dialect maps. Then, the Fuzzy C-Means clustering method is applied to these covariance data. Thereby, one is able to detect and measure gradual similarities between maps. By employing the method on lexical data from the dialect atlas Sprachatlas von Bayerisch-Schwaben, it can be shown that clusters of spatially similar maps also share semantic similarities. This method can thus be used for grouping maps based on spatial similarities while at the same time indicating patterns of semantic relationships between spatially related variables.

scholarly journals A Dataset of Annotated Omnidirectional Videos for Distancing Applications

2021 ◽  
Vol 7 (8) ◽  
pp. 158
Author(s):  
Giuseppe Mazzola ◽  
Liliana Lo Lo Presti ◽  
Edoardo Ardizzone ◽  
Marco La La Cascia
Keyword(s):  
Video Surveillance ◽  
Estimation Error ◽  
Distance Estimation ◽  
Estimation Algorithm ◽  
Research Community ◽  
New Method ◽  
Acquisition Process ◽  
Empirical Results ◽  
Bounding Boxes ◽  
Surveillance Applications

Omnidirectional (or 360°) cameras are acquisition devices that, in the next few years, could have a big impact on video surveillance applications, research, and industry, as they can record a spherical view of a whole environment from every perspective. This paper presents two new contributions to the research community: the CVIP360 dataset, an annotated dataset of 360° videos for distancing applications, and a new method to estimate the distances of objects in a scene from a single 360° image. The CVIP360 dataset includes 16 videos acquired outdoors and indoors, annotated by adding information about the pedestrians in the scene (bounding boxes) and the distances to the camera of some points in the 3D world by using markers at fixed and known intervals. The proposed distance estimation algorithm is based on geometry facts regarding the acquisition process of the omnidirectional device, and is uncalibrated in practice: the only required parameter is the camera height. The proposed algorithm was tested on the CVIP360 dataset, and empirical results demonstrate that the estimation error is negligible for distancing applications.

scholarly journals SW-SDF based privacy preserving data classification

2013 ◽  
Vol 4 (3) ◽  
pp. 813-820
Author(s):  
Kiran P ◽  
Kavya N. P.
Keyword(s):  
Privacy Preservation ◽  
Data Classification ◽  
Privacy Preserving ◽  
Information Loss ◽  
Personal Privacy ◽  
Privacy Preserving Data Mining ◽  
The Core ◽  
Sensitive Attribute ◽  
Mining Algorithms

The core objective of privacy preserving data mining is to preserve the confidentiality of individual even after mining. The basic advantage of personalized privacy preservation is that the information loss is very less as compared with other privacy preservation algorithms. These algorithms how ever have not been designed for specific mining algorithms. SW-SDF personalized privacy preservation uses two flags SW and SDF. SW is used for assigning a weight for the sensitive attribute and SDF for sensitive disclosure which is accepted from individual. In this paper we have designed an algorithm which uses SW-SDF personal privacy preservation for data classification. This method ensures privacy and classification of data.

A State Decision Tree based Backtracking Algorithm for Multi-Sensitive Attribute Privacy Preserving

2016 ◽  
Vol 8 (2) ◽  
pp. 1-11
Author(s):  
Yanchao Zhang ◽  
Qing Liu ◽  
JunJun Cheng ◽  
JiJia Yang
Keyword(s):  
Decision Tree ◽  
Original Data ◽  
Privacy Preserving ◽  
Information Loss ◽  
Published Data ◽  
Sensitive Attribute

Beyond l-diversity model, an algorithm (l-BDT) based on state decision tree is proposed in this paper, which aims at protecting multi-sensitive attributes from being attacked. The algorithm considers the whole situations in equivalence partitioning for the first, prunes the decision tree according to some conditions for the second, and screens out the method with the least information loss of equivalence partitioning for the last. The analysis and experiments show that the l-BDT algorithm has the best performance in controlling the information loss. It can be ensured that the published data is the most closed towards the original data, so as to ensure that the published data is as useful as possible.

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