Security and Privacy Preserving for IoT and 5G Networks

2022 ◽  
Keyword(s):  
Privacy Preserving ◽  
Security And Privacy ◽  
5G Networks

Privacy-Preserving Decentralized Edge Caching in 5G Networks

2021 ◽  
Author(s):  
Yiming Zeng ◽  
Yaodong Huang ◽  
Zhenhua Liu ◽  
Ji Liu ◽  
Yuanyuan Yang
Keyword(s):  
Privacy Preserving ◽  
5G Networks ◽  
Edge Caching

Toward Responsible AI: An Overview of Federated Learning for User-centered Privacy-preserving Computing

2021 ◽  
Vol 11 (3-4) ◽  
pp. 1-22
Author(s):  
Qiang Yang
Keyword(s):  
Private Information ◽  
High Performance ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Brute Force ◽  
New Paradigm ◽  
User Privacy ◽  
Data Governance ◽  
Incentive Mechanisms ◽  
Rule Out

With the rapid advances of Artificial Intelligence (AI) technologies and applications, an increasing concern is on the development and application of responsible AI technologies. Building AI technologies or machine-learning models often requires massive amounts of data, which may include sensitive, user private information to be collected from different sites or countries. Privacy, security, and data governance constraints rule out a brute force process in the acquisition and integration of these data. It is thus a serious challenge to protect user privacy while achieving high-performance models. This article reviews recent progress of federated learning in addressing this challenge in the context of privacy-preserving computing. Federated learning allows global AI models to be trained and used among multiple decentralized data sources with high security and privacy guarantees, as well as sound incentive mechanisms. This article presents the background, motivations, definitions, architectures, and applications of federated learning as a new paradigm for building privacy-preserving, responsible AI ecosystems.

Security and Privacy Challenges of Deep Learning

2020 ◽  
pp. 42-64
Author(s):  
J. Andrew Onesimu ◽  
Karthikeyan J. ◽  
D. Samuel Joshua Viswas ◽  
Robin D Sebastian
Keyword(s):  
Deep Learning ◽  
Differential Privacy ◽  
Homomorphic Encryption ◽  
Detailed Comparison ◽  
Privacy Preserving ◽  
Research Field ◽  
Security And Privacy ◽  
Security Attacks ◽  
Privacy Breaches ◽  
Comparison Table

Deep learning is the buzz word in recent times in the research field due to its various advantages in the fields of healthcare, medicine, automobiles, etc. A huge amount of data is required for deep learning to achieve better accuracy; thus, it is important to protect the data from security and privacy breaches. In this chapter, a comprehensive survey of security and privacy challenges in deep learning is presented. The security attacks such as poisoning attacks, evasion attacks, and black-box attacks are explored with its prevention and defence techniques. A comparative analysis is done on various techniques to prevent the data from such security attacks. Privacy is another major challenge in deep learning. In this chapter, the authors presented an in-depth survey on various privacy-preserving techniques for deep learning such as differential privacy, homomorphic encryption, secret sharing, and secure multi-party computation. A detailed comparison table to compare the various privacy-preserving techniques and approaches is also presented.

Secure and Privacy Preserving Keyword Search over the Large Scale Cloud Data

2016 ◽  
pp. 200-215
Author(s):  
Wei Zhang ◽  
Jie Wu ◽  
Yaping Lin
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Keyword Search ◽  
Data Encryption ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Data Utilization ◽  
Cloud Data ◽  
Efficient Resource ◽  
Efficient Data

Cloud computing has attracted a lot of interests from both the academics and the industries, since it provides efficient resource management, economical cost, and fast deployment. However, concerns on security and privacy become the main obstacle for the large scale application of cloud computing. Encryption would be an alternative way to relief the concern. However, data encryption makes efficient data utilization a challenging problem. To address this problem, secure and privacy preserving keyword search over large scale cloud data is proposed and widely developed. In this paper, we make a thorough survey on the secure and privacy preserving keyword search over large scale cloud data. We investigate existing research arts category by category, where the category is classified according to the search functionality. In each category, we first elaborate on the key idea of existing research works, then we conclude some open and interesting problems.

scholarly journals An Efficient Identity-Based Conditional Privacy-Preserving Authentication Scheme for Secure Communication in a Vehicular Ad Hoc Network

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1687 ◽  
Author(s):  
Mahmood A. Al-shareeda ◽  
Mohammed Anbar ◽  
Selvakumar Manickam ◽  
Iznan H. Hasbullah
Keyword(s):  
Secure Communication ◽  
Large Scale ◽  
Ad Hoc ◽  
Privacy Preserving ◽  
Authentication Scheme ◽  
Security And Privacy ◽  
Group Signature ◽  
Side Channel ◽  
Sensitive Information ◽  
Identity Based

The security and privacy issues in vehicular ad hoc networks (VANETs) are often addressed with schemes based on either public key infrastructure, group signature, or identity. However, none of these schemes appropriately address the efficient verification of multiple VANET messages in high-density traffic areas. Attackers could obtain sensitive information kept in a tamper-proof device (TPD) by using a side-channel attack. In this paper, we propose an identity-based conditional privacy-preserving authentication scheme that supports a batch verification process for the simultaneous verification of multiple messages by each node. Furthermore, to thwart side-channel attacks, vehicle information in the TPD is periodically and frequently updated. Finally, since the proposed scheme does not utilize the bilinear pairing operation or the Map-To-Point hash function, its performance outperforms other schemes, making it viable for large-scale VANETs deployment.

scholarly journals Toward Blockchain-Enabled Privacy-Preserving Data Transmission in Cluster-Based Vehicular Networks

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1358 ◽  
Author(s):  
Gyanendra Prasad Joshi ◽  
Eswaran Perumal ◽  
K. Shankar ◽  
Usman Tariq ◽  
Tariq Ahmad ◽  
...  
Keyword(s):  
Data Transmission ◽  
Intelligent Transportation Systems ◽  
Vehicular Ad Hoc Networks ◽  
Privacy Preserving ◽  
Transportation Systems ◽  
Security And Privacy ◽  
Driving Safety ◽  
Delivery Ratio ◽  
Blockchain Technology ◽  
On The Road

In recent times, vehicular ad hoc networks (VANET) have become a core part of intelligent transportation systems (ITSs), which aim to achieve continual Internet connectivity among vehicles on the road. The VANET has been used to improve driving safety and construct an ITS in modern cities. However, owing to the wireless characteristics, the message transmitted through the network can be observed, altered, or forged. Since driving safety is a major part of VANET, the security and privacy of these messages must be preserved. Therefore, this paper introduces an efficient privacy-preserving data transmission architecture that makes use of blockchain technology in cluster-based VANET. The cluster-based VANET architecture is used to achieve load balancing and minimize overhead in the network, where the clustering process is performed using the rainfall optimization algorithm (ROA). The ROA-based clustering with blockchain-based data transmission, called a ROAC-B technique, initially clusters the vehicles, and communication takes place via blockchain technology. A sequence of experiments was conducted to ensure the superiority of the ROAC-B technique, and several aspects of the results were considered. The simulation outcome showed that the ROAC-B technique is superior to other techniques in terms of packet delivery ratio (PDR), end to end (ETE) delay, throughput, and cluster size.

scholarly journals A Fine-Grained User-Divided Privacy-Preserving Access Control Protocol in Smart Watch

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2109
Author(s):  
Liming Fang ◽  
Minghui Li ◽  
Lu Zhou ◽  
Hanyi Zhang ◽  
Chunpeng Ge
Keyword(s):  
Access Control ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Data Access ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Fine Grained ◽  
Data Access Control ◽  
Smart Watch ◽  
Performance And Evaluation

A smart watch is a kind of emerging wearable device in the Internet of Things. The security and privacy problems are the main obstacles that hinder the wide deployment of smart watches. Existing security mechanisms do not achieve a balance between the privacy-preserving and data access control. In this paper, we propose a fine-grained privacy-preserving access control architecture for smart watches (FPAS). In FPAS, we leverage the identity-based authentication scheme to protect the devices from malicious connection and policy-based access control for data privacy preservation. The core policy of FPAS is two-fold: (1) utilizing a homomorphic and re-encrypted scheme to ensure that the ciphertext information can be correctly calculated; (2) dividing the data requester by different attributes to avoid unauthorized access. We present a concrete scheme based on the above prototype and analyze the security of the FPAS. The performance and evaluation demonstrate that the FPAS scheme is efficient, practical, and extensible.

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