scholarly journals Differentially Private Web Browsing Trajectory over Infinite Streams

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiang Liu ◽  
Yuchun Guo ◽  
Xiaoying Tan ◽  
Yishuai Chen
Keyword(s):  
Data Mining ◽  
Theoretical Analysis ◽  
Differential Privacy ◽  
Data Accuracy ◽  
Web Browsing ◽  
Web Traffic ◽  
Data Utility ◽  
Privacy Model ◽  
Content Popularity ◽  
Popularity Prediction

Nowadays, a lot of data mining applications, such as web traffic analysis and content popularity prediction, leverage users’ web browsing trajectories to improve their performance. However, the disclosure of web browsing trajectory is the most prominent issue. A novel privacy model, named Differential Privacy, is used to rigorously protect user’s privacy. Some works have applied this privacy model to spatial-temporal streams. However, these works either protect the users’ activities in different places separately or protect their activities in all places jointly. The former one cannot protect trajectories that traverse multiple places; while the latter ignores the differences among places and suffers the degradation of data utility (i.e., data accuracy). In this paper, we propose a w , n -differential privacy to protect any spatial-temporal sequence occurring in w successive timestamps and n -range places. To achieve better data utility, we propose two implementation algorithms, named Spatial-Temporal Budget Distribution (STBD) and Spatial-Temporal RescueDP (STR). Theoretical analysis and experimental results show that these two algorithms can achieve a balance between data utility and trajectory privacy guarantee.

scholarly journals Secure Deep Graph Generation with Link Differential Privacy

2021 ◽  
Author(s):  
Carl Yang ◽  
Haonan Wang ◽  
Ke Zhang ◽  
Liang Chen ◽  
Lichao Sun
Keyword(s):  
Data Mining ◽  
Differential Privacy ◽  
Structure Learning ◽  
Oriented Graph ◽  
Structured Data ◽  
Generation Model ◽  
Data Utility ◽  
Relational Structures ◽  
Privacy Constraints ◽  
Graph Generation

Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes). Since relational data are often sensitive, we aim to seek effective approaches to generate utility-preserved yet privacy-protected structured data. In this paper, we leverage the differential privacy (DP) framework to formulate and enforce rigorous privacy constraints on deep graph generation models, with a focus on edge-DP to guarantee individual link privacy. In particular, we enforce edge-DP by injecting designated noise to the gradients of a link reconstruction based graph generation model, while ensuring data utility by improving structure learning with structure-oriented graph discrimination. Extensive experiments on two real-world network datasets show that our proposed DPGGAN model is able to generate graphs with effectively preserved global structure and rigorously protected individual link privacy.

scholarly journals Content Popularity Prediction Towards Location-Aware Mobile Edge Caching

2019 ◽  
Vol 21 (4) ◽  
pp. 915-929 ◽  
Author(s):  
Peng Yang ◽  
Ning Zhang ◽  
Shan Zhang ◽  
Li Yu ◽  
Junshan Zhang ◽  
...  
Keyword(s):  
Location Aware ◽  
Content Popularity ◽  
Popularity Prediction ◽  
Edge Caching

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.

Privacy Preserving Data Mining on Unstructured Data

2017 ◽  
pp. 167-190
Author(s):  
Trupti Vishwambhar Kenekar ◽  
Ajay R. Dani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Structure Data ◽  
Data Privacy ◽  
Differential Privacy ◽  
Unstructured Data ◽  
Map Reduce ◽  
Individual Data ◽  
Data Set ◽  
Privacy Preserving Data Mining

As Big Data is group of structured, unstructured and semi-structure data collected from various sources, it is important to mine and provide privacy to individual data. Differential Privacy is one the best measure which provides strong privacy guarantee. The chapter proposed differentially private frequent item set mining using map reduce requires less time for privately mining large dataset. The chapter discussed problem of preserving data privacy, different challenges to preserving data privacy in big data environment, Data privacy techniques and their applications to unstructured data. The analyses of experimental results on structured and unstructured data set are also presented.

Privacy Preserving Approaches for Online Social Network Data Publishing

2021 ◽  
pp. 119-132
Author(s):  
Kamalkumar Macwan ◽  
Sankita Patel
Keyword(s):  
Social Network ◽  
Privacy Preservation ◽  
Differential Privacy ◽  
Online Social Network ◽  
Data Publishing ◽  
Network Data ◽  
User Privacy ◽  
Social Network Data ◽  
Huge Data ◽  
Popularity Prediction

Recently, the social network platforms have gained the attention of people worldwide. People post, share, and update their views freely on such platforms. The huge data contained on social networks are utilized for various purposes like research, market analysis, product popularity, prediction, etc. Although it provides so much useful information, it raises the issue regarding user privacy. This chapter discusses the various privacy preservation methods applied to the original social network dataset to preserve privacy against attacks. The two areas for privacy preservation approaches addressed in this chapter are anonymization in social network data publication and differential privacy in node degree publishing.

Joint User Access Mode Selection and Content Popularity Prediction in Non-Orthogonal Multiple Access-Based F-RANs

2020 ◽  
Vol 68 (1) ◽  
pp. 654-666
Author(s):  
Shi Yan ◽  
Lin Qi ◽  
Yangcheng Zhou ◽  
Mugen Peng ◽  
G. M. Shafiqur Rahman
Keyword(s):  
Multiple Access ◽  
Mode Selection ◽  
Access Mode ◽  
Content Popularity ◽  
User Access ◽  
Popularity Prediction

Sensor Field Resource Management for Sensor Network Data Mining

2010 ◽  
pp. 280-304
Author(s):  
David J. Yates ◽  
Jennifer Xu
Keyword(s):  
Data Mining ◽  
Response Time ◽  
Data Quality ◽  
Real Time ◽  
Sensor Network ◽  
Resource Consumption ◽  
Data Accuracy ◽  
Network Data ◽  
Sensor Field ◽  
User Queries

This research is motivated by data mining for wireless sensor network applications. The authors consider applications where data is acquired in real-time, and thus data mining is performed on live streams of data rather than on stored databases. One challenge in supporting such applications is that sensor node power is a precious resource that needs to be managed as such. To conserve energy in the sensor field, the authors propose and evaluate several approaches to acquiring, and then caching data in a sensor field data server. The authors show that for true real-time applications, for which response time dictates data quality, policies that emulate cache hits by computing and returning approximate values for sensor data yield a simultaneous quality improvement and cost saving. This “win-win” is because when data acquisition response time is sufficiently important, the decrease in resource consumption and increase in data quality achieved by using approximate values outweighs the negative impact on data accuracy due to the approximation. In contrast, when data accuracy drives quality, a linear trade-off between resource consumption and data accuracy emerges. The authors then identify caching and lookup policies for which the sensor field query rate is bounded when servicing an arbitrary workload of user queries. This upper bound is achieved by having multiple user queries share the cost of a sensor field query. Finally, the authors discuss the challenges facing sensor network data mining applications in terms of data collection, warehousing, and mining techniques.

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