Assisted Fog Computing Approach for Data Privacy Preservation in IoT-Based Healthcare

2021 ◽  
pp. 191-201
Author(s):  
Mohamed Sarrab ◽  
Fatma Alshohoumi
Keyword(s):  
Data Privacy ◽  
Privacy Preservation ◽  
Fog Computing ◽  
Computing Approach

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

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.

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

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.

scholarly journals Privacy-Preserving Efficient Data Retrieval in IoMT Based on Low-Cost Fog Computing

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Na Wang ◽  
Yuanyuan Cai ◽  
Junsong Fu ◽  
Jie Xu
Keyword(s):  
Energy Cost ◽  
Data Privacy ◽  
Low Cost ◽  
Rapid Development ◽  
Fog Computing ◽  
Bat Algorithm ◽  
Data Retrieval ◽  
High Energy ◽  
Physiological Data ◽  
Range Tree

The rapid development of Internet of Medical Things (IoMT) is remarkable. However, IoMT faces many problems including privacy disclosure, long delay of service orders, low retrieval efficiency of medical data, and high energy cost of fog computing. For these, this paper proposes a data privacy protection and efficient retrieval scheme for IoMT based on low-cost fog computing. First, a fog computing system is located between a cloud server and medical workers, for processing data retrieval requests of medical workers and orders for controlling medical devices. Simultaneously, it preprocesses physiological data of patients uploaded by IoMT, collates them into various data sets, and transmits them to medical institutions in this way. It makes the entire execution process of low latency and efficient. Second, multidimensional physiological data are of great value, and we use ciphertext retrieval to protect privacy of patient data in this paper. In addition, this paper uses range tree to build an index for storing physiological data vectors, and meanwhile a range retrieval method is also proposed to improve data search efficiency. Finally, bat algorithm (BA) is designed to allocate cost on a fog server group for significant energy cost reduction. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.

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

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.

scholarly journals Opposition Intensity-Based Cuckoo Search Algorithm for Data Privacy Preservation

2019 ◽  
Vol 29 (1) ◽  
pp. 1441-1452 ◽  
Author(s):  
G.K. Shailaja ◽  
C.V. Guru Rao
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Original Data ◽  
Cuckoo Search Algorithm ◽  
Research Issues ◽  
Novel Approach ◽  
Two Phases

Abstract Privacy-preserving data mining (PPDM) is a novel approach that has emerged in the market to take care of privacy issues. The intention of PPDM is to build up data-mining techniques without raising the risk of mishandling of the data exploited to generate those schemes. The conventional works include numerous techniques, most of which employ some form of transformation on the original data to guarantee privacy preservation. However, these schemes are quite multifaceted and memory intensive, thus leading to restricted exploitation of these methods. Hence, this paper intends to develop a novel PPDM technique, which involves two phases, namely, data sanitization and data restoration. Initially, the association rules are extracted from the database before proceeding with the two phases. In both the sanitization and restoration processes, key extraction plays a major role, which is selected optimally using Opposition Intensity-based Cuckoo Search Algorithm, which is the modified format of Cuckoo Search Algorithm. Here, four research issues, such as hiding failure rate, information preservation rate, and false rule generation, and degree of modification are minimized using the adopted sanitization and restoration processes.

An SOA-Based Architecture to Share Medical Data with Privacy Preservation

2011 ◽  
Vol 2 (3) ◽  
pp. 11-26
Author(s):  
Mahmoud Barhamgi ◽  
Djamal Benslimane ◽  
Chirine Ghedira ◽  
Brahim Medjahed
Keyword(s):  
Web Services ◽  
Web Service ◽  
Data Sharing ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Medical Data ◽  
Query Rewriting ◽  
Healthcare Organizations ◽  
Privacy Constraints ◽  
Viable Approach

Recent years have witnessed a growing interest in using Web services as a reliable means for medical data sharing inside and across healthcare organizations. In such service-based data sharing environments, Web service composition emerged as a viable approach to query data scattered across independent locations. Patient data privacy preservation is an important aspect that must be considered when composing medical Web services. In this paper, the authors show how data privacy can be preserved when composing and executing Web services. Privacy constraints are expressed in the form of RDF queries over a mediated ontology. Query rewriting algorithms are defined to process those queries while preserving users’ privacy.

Privacy Preserving Big Data Publishing

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.

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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