scholarly journals Analysis of centrality measures under differential privacy models

2022 ◽  
Vol 412 ◽  
pp. 126546
Author(s):  
Jesse Laeuchli ◽  
Yunior Ramírez-Cruz ◽  
Rolando Trujillo-Rasua
Keyword(s):  
Differential Privacy ◽  
Centrality Measures ◽  
Privacy Models

scholarly journals A Survey on Differential Privacy for Unstructured Data Content

2022 ◽  
Author(s):  
Ying Zhao ◽  
Jinjun Chen
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Personal Information ◽  
Unstructured Data ◽  
Future Research ◽  
Huge Amount ◽  
Protection Technology ◽  
Data Content ◽  
Human Faces ◽  
Privacy Models

Huge amount of unstructured data including image, video, audio, and text are ubiquitously generated and shared, it is a challenge to protect sensitive personal information in them, such as human faces, voiceprints, and authorships. Differential privacy is the standard privacy protection technology that provides rigorous privacy guarantees for various data. This survey summarizes and analyzes differential privacy solutions to protect unstructured data content before they are shared with untrusted parties. These differential privacy methods obfuscate unstructured data after they are represented with vectors, and then reconstruct them with obfuscated vectors. We summarize specific privacy models and mechanisms together with possible challenges in them. We also conclude their privacy guarantees against AI attacks and utility losses. Finally, we discuss several possible directions for future research.

Communicating Differential Privacy Models by Illustrations: A Survey and In-Depth Interview Study

2021 ◽  
Vol 65 (1) ◽  
pp. 1494-1494
Author(s):  
Chuhao Wu ◽  
Tianhao Wang ◽  
Robert W. Proctor ◽  
Ninghui Li ◽  
Jeremiah Blocki ◽  
...  
Keyword(s):  
Differential Privacy ◽  
Interview Study ◽  
Depth Interview ◽  
Privacy Models

Analysis of Oncogene Protein Structure Using Small World Network Concept

2020 ◽  
Vol 15 (7) ◽  
pp. 732-740
Author(s):  
Neetu Kumari ◽  
Anshul Verma
Keyword(s):  
Amino Acids ◽  
Protein Structure ◽  
Degree Distribution ◽  
Protein Structures ◽  
Small World ◽  
Extreme Condition ◽  
Centrality Measures ◽  
Small World Network ◽  
Network Concept ◽  
Oncogene Protein

Background: The basic building block of a body is protein which is a complex system whose structure plays a key role in activation, catalysis, messaging and disease states. Therefore, careful investigation of protein structure is necessary for the diagnosis of diseases and for the drug designing. Protein structures are described at their different levels of complexity: primary (chain), secondary (helical), tertiary (3D), and quaternary structure. Analyzing complex 3D structure of protein is a difficult task but it can be analyzed as a network of interconnection between its component, where amino acids are considered as nodes and interconnection between them are edges. Objective: Many literature works have proven that the small world network concept provides many new opportunities to investigate network of biological systems. The objective of this paper is analyzing the protein structure using small world concept. Methods: Protein is analyzed using small world network concept, specifically where extreme condition is having a degree distribution which follows power law. For the correct verification of the proposed approach, dataset of the Oncogene protein structure is analyzed using Python programming. Results: Protein structure is plotted as network of amino acids (Residue Interaction Graph (RIG)) using distance matrix of nodes with given threshold, then various centrality measures (i.e., degree distribution, Degree-Betweenness correlation, and Betweenness-Closeness correlation) are calculated for 1323 nodes and graphs are plotted. Conclusion: Ultimately, it is concluded that there exist hubs with higher centrality degree but less in number, and they are expected to be robust toward harmful effects of mutations with new functions.

Centrality Measures

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
State Of The Art ◽  
Centrality Measures ◽  
Eigenvector Centrality ◽  
Multilayer Networks ◽  
Comprehensive Description ◽  
Multilayer Network ◽  
Suitable Definition ◽  
Katz Centrality ◽  
Pagerank Centrality

Defining the centrality of nodes and layers in multilayer networks is of fundamental importance for a variety of applications from sociology to biology and finance. This chapter presents the state-of-the-art centrality measures able to characterize the centrality of nodes, the influences of layers or the centrality of replica nodes in multilayer and multiplex networks. These centrality measures include modifications of the eigenvector centrality, Katz centrality, PageRank centrality and Communicability to the multilayer network scenario. The chapter provides a comprehensive description of the research of the field and discusses the main advantages and limitations of the different definitions, allowing the readers that wish to apply these techniques to choose the most suitable definition for his or her case study.

Three Perspectives on Centrality

2020 ◽  
pp. 333-351
Author(s):  
Stephen P. Borgatti ◽  
Martin G. Everett
Keyword(s):  
Centrality Measures ◽  
Graph Invariant ◽  
Propagation Process ◽  
Centrality Measure ◽  
The Third ◽  
First Arrival ◽  
The Difference

This chapter presents three different perspectives on centrality. In part, the motivation is definitional: what counts as a centrality measure and what doesn’t? But the primary purpose is to lay out ways that centrality measures are similar and dissimilar and point to appropriate ways of interpreting different measures. The first perspective the chapter considers is the “walk structure participation” perspective. In this perspective, centrality measures indicate the extent and manner in which a node participates in the walk structure of a graph. A typology is presented that distinguishes measures based on dimensions such as (1) what kinds of walks are considered (e.g., geodesics, paths, trails, or unrestricted walks) and (2) whether the number of walks is counted or the length of walks is assessed, or both. The second perspective the chapter presents is the “induced centrality” perspective, which views a node’s centrality as its contribution to a specific graph invariant—typically some measure of the cohesiveness of the network. Induced centralities are computed by calculating the graph invariant, removing the node in question, and recalculating the graph invariant. The difference is the node’s centrality. The third perspective is the “flow outcomes” perspective. Here the chapter views centralities as estimators of node outcomes in some kind of propagation process. Generic node outcomes include how often a bit of something propagating passes through a node and the time until first arrival of something flowing. The latter perspective leads us to consider the merits of developing custom measures for different research settings versus using off-the-shelf measures that were not necessarily designed for the current purpose.

Sign in / Sign up

Export Citation Format

Share Document