scholarly journals Achieving High Data Utility K-Anonymization Using Similarity-Based Clustering Model

2016 ◽  
Vol E99.D (8) ◽  
pp. 2069-2078 ◽  
Author(s):  
Mohammad Rasool SARRAFI AGHDAM ◽  
Noboru SONEHARA
Keyword(s):  
Data Utility ◽  
High Data ◽  
Clustering Model

scholarly journals Differentially Private Attributed Network Releasing Based on Early Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yuye Wang ◽  
Jing Yang ◽  
Jianpei Zhan
Keyword(s):  
Topological Structure ◽  
Differential Privacy ◽  
Probability Model ◽  
Prior Work ◽  
Data Utility ◽  
High Data ◽  
Early Fusion ◽  
Attributed Network ◽  
Analysis Of Social Networks ◽  
Attributed Networks

Vertex attributes exert huge impacts on the analysis of social networks. Since the attributes are often sensitive, it is necessary to seek effective ways to protect the privacy of graphs with correlated attributes. Prior work has focused mainly on the graph topological structure and the attributes, respectively, and combining them together by defining the relevancy between them. However, these methods need to add noise to them, respectively, and they produce a large number of required noise and reduce the data utility. In this paper, we introduce an approach to release graphs with correlated attributes under differential privacy based on early fusion. We combine the graph topological structure and the attributes together with a private probability model and generate a synthetic network satisfying differential privacy. We conduct extensive experiments to demonstrate that our approach could meet the request of attributed networks and achieve high data utility.

scholarly journals Quality Indicators for Statistical Disclosure Methods: A Case Study on the Structure of Earnings Survey

2015 ◽  
Vol 31 (4) ◽  
pp. 737-761 ◽  
Author(s):  
Matthias Templ
Keyword(s):  
Original Data ◽  
Data Sets ◽  
Data Utility ◽  
High Data ◽  
Disclosure Risk ◽  
Statistical Disclosure ◽  
Context Data ◽  
Statistical Agencies ◽  
Utility Measures

Abstract Scientific- or public-use files are typically produced by applying anonymisation methods to the original data. Anonymised data should have both low disclosure risk and high data utility. Data utility is often measured by comparing well-known estimates from original data and anonymised data, such as comparing their means, covariances or eigenvalues. However, it is a fact that not every estimate can be preserved. Therefore the aim is to preserve the most important estimates, that is, instead of calculating generally defined utility measures, evaluation on context/data dependent indicators is proposed. In this article we define such indicators and utility measures for the Structure of Earnings Survey (SES) microdata and proper guidelines for selecting indicators and models, and for evaluating the resulting estimates are given. For this purpose, hundreds of publications in journals and from national statistical agencies were reviewed to gain insight into how the SES data are used for research and which indicators are relevant for policy making. Besides the mathematical description of the indicators and a brief description of the most common models applied to SES, four different anonymisation procedures are applied and the resulting indicators and models are compared to those obtained from the unmodified data. The disclosure risk is reported and the data utility is evaluated for each of the anonymised data sets based on the most important indicators and a model which is often used in practice.

A secureK-automorphism privacy preserving approach with high data utility in social networks

2013 ◽  
Vol 7 (9) ◽  
pp. 1399-1411 ◽  
Author(s):  
Jun Yang ◽  
Bin Wang ◽  
Xiaochun Yang ◽  
Hongyi Zhang ◽  
Guang Xiang
Keyword(s):  
Social Networks ◽  
Privacy Preserving ◽  
Data Utility ◽  
High Data

scholarly journals Differential Privacy for Evolving Network Based on GHRG

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jing Yang ◽  
Yuye Wang ◽  
Jianpei Zhang
Keyword(s):  
Differential Privacy ◽  
Dynamic Environment ◽  
Sensitive Information ◽  
Data Utility ◽  
High Data ◽  
Evolving Networks ◽  
Calculating Method ◽  
Individual Privacy ◽  
Local Areas ◽  
Privacy Analysis

Releasing evolving networks which contain sensitive information could compromise individual privacy. In this paper, we study the problem of releasing evolving networks under differential privacy. We explore the possibility of designing a differentially private evolving networks releasing algorithm. We found that the majority of traditional methods provide a snapshot of the networks under differential privacy over a brief period of time. As the network structure only changes in local part, the amount of required noise entirely is large and it leads to an inefficient utility. To this end, we propose GHRG-DP, a novel differentially private evolving networks releasing algorithm which reduces the noise scale and achieves high data utility. In the GHRG-DP algorithm, we learn the online connection probabilities between vertices in the evolving networks by generalized hierarchical random graph (GHRG) model. To fit the dynamic environment, a dendrogram structure adjusting method in local areas is proposed to reduce the noise scale in the whole period of time. Moreover, to avoid the unhelpful outcome of the connection probabilities, a Bayesian noisy probabilities calculating method is proposed. Through formal privacy analysis, we show that the GHRG-DP algorithm is ε -differentially private. Experiments on real evolving network datasets illustrate that GHRG-DP algorithm can privately release evolving networks with high accuracy.

scholarly journals Differentially private data aggregating with relative error constraint

2021 ◽  
Author(s):  
Hao Wang ◽  
Xiao Peng ◽  
Yihang Xiao ◽  
Zhengquan Xu ◽  
Xian Chen
Keyword(s):  
Relative Error ◽  
Differential Privacy ◽  
Privacy Preserving ◽  
Statistical Properties ◽  
Data Utility ◽  
High Data ◽  
Private Data ◽  
The Mean ◽  
Error Constraint ◽  
Mean Variance

AbstractPrivacy preserving methods supporting for data aggregating have attracted the attention of researchers in multidisciplinary fields. Among the advanced methods, differential privacy (DP) has become an influential privacy mechanism owing to its rigorous privacy guarantee and high data utility. But DP has no limitation on the bound of noise, leading to a low-level utility. Recently, researchers investigate how to preserving rigorous privacy guarantee while limiting the relative error to a fixed bound. However, these schemes destroy the statistical properties, including the mean, variance and MSE, which are the foundational elements for data aggregating and analyzing. In this paper, we explore the optimal privacy preserving solution, including novel definitions and implementing mechanisms, to maintain the statistical properties while satisfying DP with a fixed relative error bound. Experimental evaluation demonstrates that our mechanism outperforms current schemes in terms of security and utility for large quantities of queries.

Interference measurements for high data rate TDMA transmission through ACTS

1998 ◽  
Author(s):  
Robert Kerczewski ◽  
Duc Ngo ◽  
Diepchi Tran ◽  
Quang Tran ◽  
Brian Kachmar
Keyword(s):  
Data Rate ◽  
High Data Rate ◽  
High Data ◽  
Interference Measurements

Matlab- Based Interference Mitigation in WPAN IR-UWB

2015 ◽  
Vol 3 (2) ◽  
pp. 1-14
Author(s):  
Abbas Saleh Hassan
Keyword(s):  
Short Range ◽  
Interference Mitigation ◽  
Impulse Radio ◽  
Ultra Wideband ◽  
Area Network ◽  
Wireless Technology ◽  
Technology System ◽  
Rake Receivers ◽  
High Data ◽  
Layer Solution

Impulse Radio - Ultra Wideband (IR-UWB) is a wireless technology system that offers a high data rate within a short range. Therefore, IR-UWB system is regarded as an excellent physical layer solution to the multi-piconet Wireless Personal Area Network (WPAN) applications. In spite of all the advantages of IR-UWB, there are several fundamental and practical challenges that need to be carefully addressed. The big and most important one among these challenges is the interference. Two types of Rake receivers are designed and simulated to highly mitigate the MUI these are (PRake receiver) and (SRake receiver).

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