Data privacy preservation in MAC aware Internet of things with optimized key generation

Despite the rapid growth and advancement in the Internet of Things (IoT ), there are critical challenges that need to be addressed before the full adoption of the IoT. Data privacy is one of the hurdles towards the adoption of IoT as there might be potential misuse of users’ data and their identity in IoT applications. Several researchers have proposed different approaches to reduce privacy risks. However, most of the existing solutions still suffer from various drawbacks, such as huge bandwidth utilization and network latency, heavyweight cryptosystems, and policies that are applied on sensor devices and in the cloud. To address these issues, fog computing has been introduced for IoT network edges providing low latency, computation, and storage services. In this survey, we comprehensively review and classify privacy requirements for an in-depth understanding of privacy implications in IoT applications. Based on the classification, we highlight ongoing research efforts and limitations of the existing privacy-preservation techniques and map the existing IoT schemes with Fog-enabled IoT schemes to elaborate on the benefits and improvements that Fog-enabled IoT can bring to preserve data privacy in IoT applications. Lastly, we enumerate key research challenges and point out future research directions.

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Analysis of Privacy-Preserving Edge Computing and Internet of Things Models in Healthcare Domain

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6834800 ◽

2021 ◽

Vol 2021 ◽

pp. 1-6

Author(s):

Naif Almusallam ◽

Abdulatif Alabdulatif ◽

Fawaz Alarfaj

Keyword(s):

Internet Of Things ◽

Data Privacy ◽

Privacy Preservation ◽

Edge Computing ◽

Healthcare Sector ◽

Healthcare Applications ◽

Preservation Solutions ◽

Computing Environments ◽

The Common ◽

The Internet Of Things

The healthcare sector is rapidly being transformed to one that operates in new computing environments. With researchers increasingly committed to finding and expanding healthcare solutions to include the Internet of Things (IoT) and edge computing, there is a need to monitor more closely than ever the data being collected, shared, processed, and stored. The advent of cloud, IoT, and edge computing paradigms poses huge risks towards the privacy of data, especially, in the healthcare environment. However, there is a lack of comprehensive research focused on seeking efficient and effective solutions that ensure data privacy in the healthcare domain. The data being collected and processed by healthcare applications is sensitive, and its manipulation by malicious actors can have catastrophic repercussions. This paper discusses the current landscape of privacy-preservation solutions in IoT and edge healthcare applications. It describes the common techniques adopted by researchers to integrate privacy in their healthcare solutions. Furthermore, the paper discusses the limitations of these solutions in terms of their technical complexity, effectiveness, and sustainability. The paper closes with a summary and discussion of the challenges of safeguarding privacy in IoT and edge healthcare solutions which need to be resolved for future applications.

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An Improvised Framework for Privacy Preservation in IoT

Research Anthology on Privatizing and Securing Data ◽

10.4018/978-1-7998-8954-0.ch022 ◽

2021 ◽

pp. 475-491

Author(s):

Muzzammil Hussain ◽

Neha Kaliya

Keyword(s):

Big Data ◽

Internet Of Things ◽

Execution Time ◽

Data Privacy ◽

Privacy Preservation ◽

Encryption Algorithm ◽

The Public ◽

Attribute Based Encryption ◽

Key Length ◽

Different Levels

Data privacy is now-a-days a special issue in era of Internet of Things because of the big data stored and transmitted by the public/private devices. Different types and levels of privacy can be provided at different layers of IoT architecture, also different mechanisms operate at different layers of IoT architecture. This article presents the work being done towards the design of a generic framework to integrate these privacy preserving mechanisms at different layers of IoT architecture and can ensure privacy preservation in a heterogeneous IoT environment. The data is classified into different levels of secrecy and appropriate rules and mechanisms are applied to ensure this privacy. The proposed framework is implemented and evaluated for its performance with security and execution time or primary parameters. Various scenarios are also evaluated, and a comparison is done with an existing mechanism ABE (Attribute Based Encryption). It has been found that the proposed work takes less time and is more secure due to short key length and randomness of the parameters used in encryption algorithm.

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Big Data Privacy Preservation Using Two Phase Top-Down Specialization Algorithm with Multidimensional Map Reduce Framework on Hadoop

International Journal of Distributed and Cloud Computing ◽

10.21863/ijdcc/2015.3.2.009 ◽

2015 ◽

Vol 3 (2) ◽

Author(s):

Shalin Eliabeth S. ◽

Sarju S.

Keyword(s):

Big Data ◽

Data Privacy ◽

Privacy Preservation ◽

Experimental Result ◽

Map Reduce ◽

Distributed Environment ◽

Top Down ◽

Two Phase ◽

Data Anonymization ◽

Big Data Privacy

Big data privacy preservation is one of the most disturbed issues in current industry. Sometimes the data privacy problems never identified when input data is published on cloud environment. Data privacy preservation in hadoop deals in hiding and publishing input dataset to the distributed environment. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, many cloud applications with big data anonymization faces the same kind of problems. For recovering this kind of problems, here introduced a data anonymization algorithm called Two Phase Top-Down Specialization (TPTDS) algorithm that is implemented in hadoop. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization in map reduce framework, here implemented proposed Two-Phase Top-Down Specialization anonymization algorithm in hadoop and it will increases the efficiency on the big data processing system. By conducting experiment in both one dimensional and multidimensional map reduce framework with Two Phase Top-Down Specialization algorithm on hadoop, the better result shown in multidimensional anonymization on input adult dataset. Data sets is generalized in a top-down manner and the better result was shown in multidimensional map reduce framework by the better IGPL values generated by the algorithm. The anonymization was performed with specialization operation on taxonomy tree. The experiment shows that the solutions improves the IGPL values, anonymity parameter and decreases the execution time of big data privacy preservation by compared to the existing algorithm. This experimental result will leads to great application to the distributed environment.

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Smart Factories, Dumb Policy?: Managing Cybersecurity and Data Privacy Risks in the Industrial Internet of Things

SSRN Electronic Journal ◽

10.2139/ssrn.3252498 ◽

2018 ◽

Cited By ~ 1

Author(s):

Scott J. Shackelford

Keyword(s):

Internet Of Things ◽

Data Privacy ◽

Industrial Internet Of Things ◽

Industrial Internet ◽

Privacy Risks ◽

Smart Factories

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LRDM: Local Record-Driving Mechanism for Big Data Privacy Preservation in Social Networks

2016 IEEE First International Conference on Data Science in Cyberspace (DSC) ◽

10.1109/dsc.2016.94 ◽

2016 ◽

Cited By ~ 2

Author(s):

Weihao Li ◽

Hui Li

Keyword(s):

Social Networks ◽

Big Data ◽

Data Privacy ◽

Privacy Preservation ◽

Driving Mechanism ◽

Big Data Privacy

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LQI-Key: Symmetric Key Generation Scheme for Internet-of-Things (IoT) Devices Using Wireless Channel Link Quality

2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) ◽

10.1109/icaecc.2018.8479465 ◽

2018 ◽

Cited By ~ 1

Author(s):

Nandini Kuruwatti ◽

Y. N. Nayana ◽

Nikhita Sarole ◽

Girish Revadigar ◽

Chitra Javali

Keyword(s):

Internet Of Things ◽

Wireless Channel ◽

Link Quality ◽

Key Generation ◽

Symmetric Key ◽

Iot Devices

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Improved Privacy Preservation Framework for Cloud-Based Internet of Things

Internet of Things ◽

10.1201/9781003032441-11 ◽

2020 ◽

pp. 165-174

Author(s):

N. Yuvaraj ◽

R. Arshath Raja ◽

T. Karthikeyan ◽

N. V. Kousik

Keyword(s):

Internet Of Things ◽

Privacy Preservation

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LLSFIoT: Lightweight Logical Security Framework for Internet of Things

Wireless Communications and Mobile Computing ◽

10.1155/2021/8526206 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Isha Batra ◽

Hatem S. A. Hamatta ◽

Arun Malik ◽

Mohammed Baz ◽

Fahad R. Albogamy ◽

...

Keyword(s):

Internet Of Things ◽

Data Security ◽

Algorithm Design ◽

Research Work ◽

Generation Mechanism ◽

Key Generation ◽

Security Framework ◽

Heavy Weight ◽

Work First ◽

Security Enhancements

Current research in Internet of Things (IoT) is focused on the security enhancements to every communicated message in the network. Keeping this thought in mind, researcher in this work emphasizes on a security oriented cryptographic solution. Commonly used security cryptographic solutions are heavy in nature considering their key size, operations, and mechanism they follow to secure a message. This work first determines the benefit of applying lightweight security cryptographic solutions in IoT. The existing lightweight counterparts are still vulnerable to attacks and also consume calculative more power. Therefore, this research work proposes a new hybrid lightweight logical security framework for offering security in IoT (LLSFIoT). The operations, key size, and mechanism used in the proposed framework make its lightweight. The proposed framework is divided into three phases: registration, authentication, and light data security (LDS). LDS offers security by using unique keys at each round bearing small size. Key generation mechanism used is comparatively fast making the compromise of keys as a difficult task. These steps followed in the proposed algorithm design make it lightweight and a better solution for IoT-based networks as compared to the existing solutions that are relatively heavy weight in nature.

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CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/467 ◽

2020 ◽

Author(s):

Yuanrui Dong ◽

Peng Zhao ◽

Hanqiao Yu ◽

Cong Zhao ◽

Shusen Yang

Keyword(s):

Deep Learning ◽

Classification Accuracy ◽

Data Privacy ◽

Privacy Preservation ◽

Fine Tuning ◽

Quantization Scheme ◽

Raw Data ◽

Resource Limited ◽

Lower Accuracy ◽

Bandwidth Consumption

The emerging edge-cloud collaborative Deep Learning (DL) paradigm aims at improving the performance of practical DL implementations in terms of cloud bandwidth consumption, response latency, and data privacy preservation. Focusing on bandwidth efficient edge-cloud collaborative training of DNN-based classifiers, we present CDC, a Classification Driven Compression framework that reduces bandwidth consumption while preserving classification accuracy of edge-cloud collaborative DL. Specifically, to reduce bandwidth consumption, for resource-limited edge servers, we develop a lightweight autoencoder with a classification guidance for compression with classification driven feature preservation, which allows edges to only upload the latent code of raw data for accurate global training on the Cloud. Additionally, we design an adjustable quantization scheme adaptively pursuing the tradeoff between bandwidth consumption and classification accuracy under different network conditions, where only fine-tuning is required for rapid compression ratio adjustment. Results of extensive experiments demonstrate that, compared with DNN training with raw data, CDC consumes 14.9× less bandwidth with an accuracy loss no more than 1.06%, and compared with DNN training with data compressed by AE without guidance, CDC introduces at least 100% lower accuracy loss.

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