An Improvised Framework for Privacy Preservation in IoT

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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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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Privacy Preserving Big Data Publishing

Research Anthology on Privatizing and Securing Data ◽

10.4018/978-1-7998-8954-0.ch060 ◽

2021 ◽

pp. 1281-1298

Author(s):

Nancy Victor ◽

Daphne Lopez

Keyword(s):

Big Data ◽

Language Processing ◽

Data Privacy ◽

Privacy Preservation ◽

Personal Data ◽

Privacy Preserving ◽

Data Publishing ◽

Time Data ◽

Digital World ◽

Distributed Framework

Data privacy plays a noteworthy part in today's digital world where information is gathered at exceptional rates from different sources. Privacy preserving data publishing refers to the process of publishing personal data without questioning the privacy of individuals in any manner. A variety of approaches have been devised to forfend consumer privacy by applying traditional anonymization mechanisms. But these mechanisms are not well suited for Big Data, as the data which is generated nowadays is not just structured in manner. The data which is generated at very high velocities from various sources includes unstructured and semi-structured information, and thus becomes very difficult to process using traditional mechanisms. This chapter focuses on the various challenges with Big Data, PPDM and PPDP techniques for Big Data and how well it can be scaled for processing both historical and real-time data together using Lambda architecture. A distributed framework for privacy preservation in Big Data by combining Natural language processing techniques is also proposed in this chapter.

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Attribute-Based Authentication Protocol of the Internet of Things

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.765-767.1726 ◽

2013 ◽

Vol 765-767 ◽

pp. 1726-1729

Author(s):

Yan Bing Liu ◽

Wen Jing Ren

Keyword(s):

Internet Of Things ◽

Authentication Protocol ◽

Security And Privacy ◽

The Internet ◽

Security Level ◽

The Public ◽

The Core ◽

Attribute Based Encryption ◽

Identity Based ◽

The Internet Of Things

Security and privacy is always the most important issues by the public in the Internet of Things. The core problems are associated with the diversifying of the Internet towards an Internet of things, and the different requirements to the security level for application. Therefore, this paper is to put forward an authentication model and protocol to cope with the problem. The protocol is adopted with attribute-based encryption to replace the traditional identity-based encryption (IBE), and then make formalization analysis to the security of the protocol by using BAN logic.

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Big Data Privacy Preservation for Cyber-Physical Systems

10.1007/978-3-030-13370-2 ◽

2019 ◽

Cited By ~ 1

Author(s):

Miao Pan ◽

Jingyi Wang ◽

Sai Mounika Errapotu ◽

Xinyue Zhang ◽

Jiahao Ding ◽

...

Keyword(s):

Big Data ◽

Data Privacy ◽

Privacy Preservation ◽

Cyber Physical Systems ◽

Physical Systems ◽

Big Data Privacy

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D2D Big Data Privacy-Preserving Framework Based on (a, k)-Anonymity Model

Mathematical Problems in Engineering ◽

10.1155/2019/2076542 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Jie Wang ◽

Hongtao Li ◽

Feng Guo ◽

Wenyin Zhang ◽

Yifeng Cui

Keyword(s):

Big Data ◽

Private Information ◽

Data Privacy ◽

Privacy Preservation ◽

Computing Time ◽

Privacy Preserving ◽

D2d Communication ◽

Group Data ◽

Big Data Privacy ◽

Daunting Challenge

As a novel and promising technology for 5G networks, device-to-device (D2D) communication has garnered a significant amount of research interest because of the advantages of rapid sharing and high accuracy on deliveries as well as its variety of applications and services. Big data technology offers unprecedented opportunities and poses a daunting challenge to D2D communication and sharing, where the data often contain private information concerning users or organizations and thus are at risk of being leaked. Privacy preservation is necessary for D2D services but has not been extensively studied. In this paper, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. Firstly, we provide a framework for the D2D big data sharing and analyze the threat model. Then, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. In our privacy-preserving framework, we adopt (a, k)-anonymity as privacy-preserving model for D2D big data and use the distributed MapReduce to classify and group data for massive datasets. The results of experiments and theoretical analysis show that our privacy-preserving algorithm deployed on MapReduce is effective for D2D big data privacy protection with less information loss and computing time.

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Internet of Things Services, Applications, Issues, and Challenges

The Internet of Things ◽

10.4018/978-1-5225-1832-7.ch023 ◽

2017 ◽

pp. 491-506

Author(s):

Padmalaya Nayak

Keyword(s):

Big Data ◽

Internet Of Things ◽

Wireless Communication ◽

Embedded Systems ◽

Data Security ◽

Data Privacy ◽

The Internet ◽

Human Intervention ◽

Unique Identifier ◽

Mechanical Devices

Internet of Things (IoT) is not a futuristic intuition, it is present everywhere. It is with devices, Sensors, Clouds, Big data, and data with business. It is the combination of traditional embedded systems combined with small wireless micro sensors, control systems with automation, and others that makes a huge infrastructure. The integration of wireless communication, micro electro mechanical devices, and Internet has led to the development of new things in the Internet. It is a network of network objects that can be accessed through the Internet and every object can be identified by unique identifier. By replacing IPV4, IPV6 plays a key role and provides a huge increase of address spaces for the development of things in the Internet. The objective of IoT application is to make the things smart without the human intervention. With the increasing number of smart nodes and amount of data that generated by each node is expected to create new concerns about data privacy, data scalability, data security, data manageability and many more issues that have been discussed in this chapter.

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Block-Chain-Based Security and Privacy in Smart City IoT

Handbook of Research on Implementation and Deployment of IoT Projects in Smart Cities - Advances in Civil and Industrial Engineering ◽

10.4018/978-1-5225-9199-3.ch009 ◽

2019 ◽

pp. 134-148

Author(s):

Thangaraj Muthuraman ◽

Punitha Ponmalar Pichiah ◽

Anuradha S.

Keyword(s):

Big Data ◽

Internet Of Things ◽

Data Communication ◽

Security And Privacy ◽

Distributed Transactions ◽

The Public ◽

Data Collection And Analysis ◽

Long Time ◽

Block Chain ◽

Iot Devices

The current technology has given arms, hands, and wings to the smart objects-internet of things, which create the centralized data collection and analysis nightmare. Even with the distributed big data-enabled computing, the relevant data filtering for the localized decisions take a long time. To make the IOT data communication smoother and make the devices talk to each other in a coherent way the device data transactions are made to communicate through the block chain, and the applications on the localized destination can take the decisions or complete transaction without the centralized hub communication. This chapter focuses on adding vendor-specific IOT devices to the public or private block chain and the emerging challenges and the possible solutions to make the devices talk to each other and have the decision enablement through the distributed transactions through the block chain technology.

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A Survey on Privacy Preservation in Fog-Enabled Internet of Things

ACM Computing Surveys ◽

10.1145/3474554 ◽

2023 ◽

Vol 55 (1) ◽

pp. 1-39

Author(s):

Kinza Sarwar ◽

Sira Yongchareon ◽

Jian Yu ◽

Saeed Ur Rehman

Keyword(s):

Internet Of Things ◽

Data Privacy ◽

Privacy Preservation ◽

Fog Computing ◽

Future Research ◽

Bandwidth Utilization ◽

Ongoing Research ◽

Iot Applications ◽

Future Research Directions ◽

Privacy Risks

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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