Data Privacy and Security Challenges for Next-Generation Aircraft: Using Smart-Bridge Technology and Privacy-Preserving Search in Heterogeneous Aircraft Systems

2017 ◽  
Author(s):  
Eric W. Rozier
Keyword(s):  
Data Privacy ◽  
Privacy Preserving ◽  
Next Generation ◽  
Privacy And Security ◽  
Aircraft Systems ◽  
Security Challenges ◽  
Bridge Technology

scholarly journals Trust Hardware Based Secured Privacy Preserving Computation System for Three-Dimensional Data

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1546
Author(s):  
Munan Yuan ◽  
Xiaofeng Li ◽  
Xiru Li ◽  
Haibo Tan ◽  
Jinlin Xu
Keyword(s):  
Data Privacy ◽  
High Performance ◽  
Distributed Storage ◽  
Three Dimensional ◽  
Privacy Preserving ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Time Consumption ◽  
Blockchain Technology ◽  
3D Data

Three-dimensional (3D) data are easily collected in an unconscious way and are sensitive to lead biological characteristics exposure. Privacy and ownership have become important disputed issues for the 3D data application field. In this paper, we design a privacy-preserving computation system (SPPCS) for sensitive data protection, based on distributed storage, trusted execution environment (TEE) and blockchain technology. The SPPCS separates a storage and analysis calculation from consensus to build a hierarchical computation architecture. Based on a similarity computation of graph structures, the SPPCS finds data requirement matching lists to avoid invalid transactions. With TEE technology, the SPPCS implements a dual hybrid isolation model to restrict access to raw data and obscure the connections among transaction parties. To validate confidential performance, we implement a prototype of SPPCS with Ethereum and Intel Software Guard Extensions (SGX). The evaluation results derived from test datasets show that (1) the enhanced security and increased time consumption (490 ms in this paper) of multiple SGX nodes need to be balanced; (2) for a single SGX node to enhance data security and preserve privacy, an increased time consumption of about 260 ms is acceptable; (3) the transaction relationship cannot be inferred from records on-chain. The proposed SPPCS implements data privacy and security protection with high performance.

scholarly journals Privacy-Aware MapReduce Based Multi-Party Secure Skyline Computation

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 207
Author(s):  
Saleh Ahmed ◽  
Mahboob Qaosar ◽  
Asif Zaman ◽  
Md. Anisuzzaman Siddique ◽  
Chen Li ◽  
...  
Keyword(s):  
Data Privacy ◽  
Large Scale ◽  
Communication Complexity ◽  
Cost Effective ◽  
Privacy Preserving ◽  
Secure Computation ◽  
Security And Privacy ◽  
Computation Method ◽  
Computation Model ◽  
Privacy And Security

Selecting representative objects from a large-scale dataset is an important task for understanding the dataset. Skyline is a popular technique for selecting representative objects from a large dataset. It is obvious that the skyline computation from the collective databases of multiple organizations is more effective than the skyline computed from a database of a single organization. However, due to privacy-awareness, every organization is also concerned about the security and privacy of their data. In this regards, we propose an efficient multi-party secure skyline computation method that computes the skyline on encrypted data and preserves the confidentiality of each party’s database objects. Although several distributed skyline computing methods have been proposed, very few of them consider the data privacy and security issues. However, privacy-preserving multi-party skyline computing techniques are not efficient enough. In our proposed method, we present a secure computation model that is more efficient in comparison with existing privacy-preserving multi-party skyline computation models in terms of computation and communication complexity. In our computation model, we also introduce MapReduce as a distributive, scalable, open-source, cost-effective, and reliable framework to handle multi-party data efficiently.

scholarly journals A Review on Security Issues and Challenges of IoT

2018 ◽  
Vol 7 (2.32) ◽  
pp. 341
Author(s):  
Dr E. Suresh Babu ◽  
V Bhargav Raj ◽  
M Manogna Devi ◽  
K Kirthana
Keyword(s):  
Secure Communication ◽  
Data Privacy ◽  
Security And Privacy ◽  
The Internet ◽  
Dynamic Nature ◽  
Privacy And Security ◽  
Security Issues ◽  
Security Challenges ◽  
Utilization Ratio ◽  
Human And Nature

This papers reviews and surveys the security issues, mechanism of internet of things (IoT) proposed in the literature. As IoT is a computing concept that describes the idea of everyday physical objects being connected to the internet and being able to identify themselves to other devices. The main advantages of IoT improve the resource utilization ratio and relationship between human and nature. In addition, it possesses various characteristics like sensing, heterogeneity, connectivity, dynamic nature and intelligence. However, it possesses several challenges like connectivity, compatibility, longevity, privacy and security. Specifically, Security and privacy are the crucial issues that need to be solved for successful deployment of IoT. Moreover, some of the data collected from the IoT are very sensitive and should not be revealed to third parties. Such data needs to be protected by some provided legislations. We reviewed some of the security challenges of IoT like secure communication, data privacy and integrity, authorizing and authenticating the devices.  

scholarly journals Privacy Preserving Decomposable Mining Association Rules on Distributed Data

2018 ◽  
Vol 7 (3.13) ◽  
pp. 157
Author(s):  
Ahmed M. Khedr ◽  
Zaher AL Aghbari ◽  
Ibrahim Kamel
Keyword(s):  
Association Rules ◽  
Data Privacy ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Data Repository ◽  
Distributed Data ◽  
Privacy And Security ◽  
Data Mining Algorithms ◽  
Computational Overhead ◽  
Mining Association Rules

In distributed computing, data sharing is inevitable, however, moving local databases from one site to another should be avoided because of the computational overhead and privacy consideration. Most of the data mining algorithms are designed assuming that data repository is stored locally. This paper presents a scheme and algorithms for mining association rules in geographically distributed data. The proposed scheme preserves data privacy of the different geographical site by passing secure messages between them. The algorithms minimize the communication cost by exchanging statistical summaries of the local databases. We provide a privacy and security analysis that shows the privacy preserving aspects of the proposed algorithms. Moreover, the paper presents extensive simulation experiments to evaluate the efficiency of the proposed scheme.  

scholarly journals Security and Privacy Requirements for Electronic Consent

2021 ◽  
Vol 20 (2) ◽  
pp. 1-24
Author(s):  
Stef Verreydt ◽  
Koen Yskout ◽  
Wouter Joosen
Keyword(s):  
Security And Privacy ◽  
Inclusion Criteria ◽  
Comprehensive Overview ◽  
Privacy And Security ◽  
Pubmed Central ◽  
Privacy Requirements ◽  
Development Lifecycle ◽  
Main Challenge ◽  
Security Challenges ◽  
Server Systems

Electronic consent (e-consent) has the potential to solve many paper-based consent approaches. Existing approaches, however, face challenges regarding privacy and security. This literature review aims to provide an overview of privacy and security challenges and requirements proposed by papers discussing e-consent implementations, as well as the manner in which state-of-the-art solutions address them. We conducted a systematic literature search using ACM Digital Library, IEEE Xplore, and PubMed Central. We included papers providing comprehensive discussions of one or more technical aspects of e-consent systems. Thirty-one papers met our inclusion criteria. Two distinct topics were identified, the first being discussions of e-consent representations and the second being implementations of e-consent in data sharing systems. The main challenge for e-consent representations is gathering the requirements for a “valid” consent. For the implementation papers, many provided some requirements but none provided a comprehensive overview. Blockchain is identified as a solution to transparency and trust issues in traditional client-server systems, but several challenges hinder it from being applied in practice. E-consent has the potential to grant data subjects control over their data. However, there is no agreed-upon set of security and privacy requirements that must be addressed by an e-consent platform. Therefore, security- and privacy-by-design techniques should be an essential part of the development lifecycle for such a platform.

Data-driven sector coupling in 5G-based smart networks

2021 ◽  
Vol 22 (1) ◽  
pp. 53-68
Author(s):  
Guenter Knieps
Keyword(s):  
Data Privacy ◽  
Service Providers ◽  
Policy Implications ◽  
Network Industries ◽  
5G Networks ◽  
Privacy And Security ◽  
Open Set ◽  
Application Service ◽  
Downstream Application

5G attains the role of a GPT for an open set of downstream IoT applications in various network industries and within the app economy more generally. Traditionally, sector coupling has been a rather narrow concept focusing on the horizontal synergies of urban system integration in terms of transport, energy, and waste systems, or else the creation of new intermodal markets. The transition toward 5G has fundamentally changed the framing of sector coupling in network industries by underscoring the relevance of differentiating between horizontal and vertical sector coupling. Due to the fixed mobile convergence and the large open set of complementary use cases, 5G has taken on the characteristics of a generalized purpose technology (GPT) in its role as the enabler of a large variety of smart network applications. Due to this vertical relationship, characterized by pervasiveness and innovational complementarities between upstream 5G networks and downstream application sectors, vertical sector coupling between the provider of an upstream GPT and different downstream application industries has acquired particular relevance. In contrast to horizontal sector coupling among different application sectors, the driver of vertical sector coupling is that each of the heterogeneous application sectors requires a critical input from the upstream 5G network provider and combines this with its own downstream technology. Of particular relevance for vertical sector coupling are the innovational complementarities between upstream GPT and downstream application sectors. The focus on vertical sector coupling also has important policy implications. Although the evolution of 5G networks strongly depends on the entrepreneurial, market-driven activities of broadband network operators and application service providers, the future of 5G as a GPT is heavily contingent on the role of frequency management authorities and European regulatory policy with regard to data privacy and security regulations.

scholarly journals Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Qi Dou ◽  
Tiffany Y. So ◽  
Meirui Jiang ◽  
Quande Liu ◽  
Varut Vardhanabhuti ◽  
...  
Keyword(s):  
Data Privacy ◽  
Medical Training ◽  
External Validation ◽  
Mainland China ◽  
Privacy Preserving ◽  
Generalization Capability ◽  
Major Focus ◽  
Sensitive Data ◽  
Lung Abnormalities ◽  
Medical Image Diagnosis

AbstractData privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.

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