scholarly journals A Novel Privacy-Preserving Scheme in IoT-Based Social Distancing Technologies

2021 ◽  
Author(s):  
Arwa Alrawais ◽  
Fatemah Alharbi ◽  
Moteeb Almoteri ◽  
Sara A Aljwair ◽  
Sara SAljwair
Keyword(s):  
Data Privacy ◽  
Personal Information ◽  
Fog Computing ◽  
Mortality Rates ◽  
Privacy Preserving ◽  
Third Party ◽  
Massive Data ◽  
Social Distancing ◽  
The World ◽  
Efficiency And Effectiveness

The COVID-19 pandemic has swapped the world, causing enormous cases, which led to high mortality rates across the globe. Internet of Things (IoT) based social distancing techniques and many current and emerging technologies have contributed to the fight against the spread of pandemics and reduce the number of positive cases. These technologies generate massive data, which will pose a significant threat to data owners’ privacy by revealing their lifestyle and personal information since that data is stored and managed by a third party like a cloud. This paper provides a new privacy-preserving scheme based on anonymization using an improved slicing technique and implying distributed fog computing. Our implementation shows that the proposed approach ensures data privacy against a third party intending to violate it for any purpose. Furthermore, our results illustrate our scheme’s efficiency and effectiveness.

scholarly journals Code-based encryption techniques with distributed cluster head and energy consumption routing protocol

2021 ◽  
Author(s):  
M. Jalasri ◽  
L. Lakshmanan
Keyword(s):  
Energy Consumption ◽  
Routing Protocol ◽  
Data Transmission ◽  
Data Security ◽  
Data Privacy ◽  
Fog Computing ◽  
Cluster Head ◽  
Third Party ◽  
Means Of Transmission ◽  
Distributed Cluster

AbstractFog computing and the Internet of Things (IoT) played a crucial role in storing data in the third-party server. Fog computing provides various resources to collect data by managing data security. However, intermediate attacks and data sharing create enormous security challenges like data privacy, confidentiality, authentication, and integrity issues. Various researchers introduce several cryptographic techniques; security is still significant while sharing data in the distributed environment. Therefore, in this paper, Code-Based Encryption with the Energy Consumption Routing Protocol (CBE-ECR) has been proposed for managing data security and data transmission protocols using keyed-hash message authentication. Initially, the data have been analyzed, and the distributed cluster head is selected, and the stochastically distributed energy clustering protocol is utilized for making the data transmission. Code-driven cryptography relies on the severity of code theory issues such as disorder demodulation and vibration required to learn equivalence. These crypto-systems are based on error codes to build a single-way function. The encryption technique minimizes intermediate attacks, and the data have protected all means of transmission. In addition to data security management, the introduced CBE-ECR reduces unauthorized access and manages the network lifetime successfully, leading to the effective data management of 96.17% and less energy consumption of 21.11% than other popular methods.The effectiveness of the system is compared to the traditional clustering techniques.

scholarly journals Cache-Based Privacy Preserving Solution for Location and Content Protection in Location-Based Services

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4651
Author(s):  
Yuanbo Cui ◽  
Fei Gao ◽  
Wenmin Li ◽  
Yijie Shi ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Location Privacy ◽  
Personal Information ◽  
Single Point ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Third Party ◽  
Content Protection ◽  
Query Privacy ◽  
Service Data

Location-Based Services (LBSs) are playing an increasingly important role in people’s daily activities nowadays. While enjoying the convenience provided by LBSs, users may lose privacy since they report their personal information to the untrusted LBS server. Although many approaches have been proposed to preserve users’ privacy, most of them just focus on the user’s location privacy, but do not consider the query privacy. Moreover, many existing approaches rely heavily on a trusted third-party (TTP) server, which may suffer from a single point of failure. To solve the problems above, in this paper we propose a Cache-Based Privacy-Preserving (CBPP) solution for users in LBSs. Different from the previous approaches, the proposed CBPP solution protects location privacy and query privacy simultaneously, while avoiding the problem of TTP server by having users collaborating with each other in a mobile peer-to-peer (P2P) environment. In the CBPP solution, each user keeps a buffer in his mobile device (e.g., smartphone) to record service data and acts as a micro TTP server. When a user needs LBSs, he sends a query to his neighbors first to seek for an answer. The user only contacts the LBS server when he cannot obtain the required service data from his neighbors. In this way, the user reduces the number of queries sent to the LBS server. We argue that the fewer queries are submitted to the LBS server, the less the user’s privacy is exposed. To users who have to send live queries to the LBS server, we employ the l-diversity, a powerful privacy protection definition that can guarantee the user’s privacy against attackers using background knowledge, to further protect their privacy. Evaluation results show that the proposed CBPP solution can effectively protect users’ location and query privacy with a lower communication cost and better quality of service.

A Survey on Privacy Preserving Dynamic Data Publishing

2021 ◽  
pp. 1635-1657
Author(s):  
Salheddine Kabou ◽  
Sidi mohamed Benslimane ◽  
Mhammed Mosteghanemi
Keyword(s):  
Data Privacy ◽  
Privacy Preservation ◽  
Personal Information ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Future Research ◽  
Dynamic Data ◽  
Practical Applications ◽  
New Process ◽  
Future Research Directions

Many organizations, especially small and medium business (SMB) enterprises require the collection and sharing of data containing personal information. The privacy of this data must be preserved before outsourcing to the commercial public. Privacy preserving data publishing PPDP refers to the process of publishing useful information while preserving data privacy. A variety of approaches have been proposed to ensure privacy by applying traditional anonymization models which focused only on the single publication of datasets. In practical applications, data publishing is more complicated where the organizations publish multiple times for different recipients or after modifications to provide up-to-date data. Privacy preserving dynamic data publication PPDDP is a new process in privacy preservation which addresses the anonymization of the data for different purposes. In this survey, the author will systematically evaluate and summarize different studies to PPDDP, clarify the differences and requirements between the scenarios that can exist, and propose future research directions.

Privacy Preserving Classification of Biomedical Data With Secure Removing of Duplicate Records

2021 ◽  
pp. 569-588
Author(s):  
Boudheb Tarik ◽  
Elberrichi Zakaria
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Third Party ◽  
Distributed Data ◽  
Biomedical Data ◽  
Collaborative Models ◽  
Highly Sensitive ◽  
Complete Access

Classifying data is to automatically assign predefined classes to data. It is one of the main applications of data mining. Having complete access to all data is critical for building accurate models. Data can be highly sensitive, such as biomedical data, which cannot be disclosed or shared with third party, because it can harm individuals and organizations. The challenge is how to preserve privacy and usefulness of data. Privacy preserving classification addresses this problem. Collaborative models are constructed over networks without violating the data owners' privacy. In this article, the authors address two problems: privacy records deduplication of the same records and privacy-preserving classification. They propose a randomized hash technic for deduplication and an enhanced privacy preserving classification of biomedical data over horizontally distributed data based on two homomorphic encryptions. No private, intermediate or final results are disclosed. Experimentations show that their solution is efficient and secure without loss of accuracy.

scholarly journals Privacy-Preserving Dynamic Personalized Pricing with Demand Learning

2021 ◽  
Author(s):  
Xi Chen ◽  
David Simchi-Levi ◽  
Yining Wang
Keyword(s):  
Dynamic Pricing ◽  
Demand Function ◽  
Differential Privacy ◽  
Personal Information ◽  
Big Data Analytics ◽  
Information Leakage ◽  
Critical Issue ◽  
Privacy Preserving ◽  
Third Party ◽  
Privacy Concern

The prevalence of e-commerce has made customers’ detailed personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When using personalized information, the question of how to protect the privacy of such information becomes a critical issue in practice. In this paper, we consider a dynamic pricing problem over T time periods with an unknown demand function of posted price and personalized information. At each time t, the retailer observes an arriving customer’s personal information and offers a price. The customer then makes the purchase decision, which will be utilized by the retailer to learn the underlying demand function. There is potentially a serious privacy concern during this process: a third-party agent might infer the personalized information and purchase decisions from price changes in the pricing system. Using the fundamental framework of differential privacy from computer science, we develop a privacy-preserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customer’s information and purchasing decisions. To this end, we first introduce a notion of anticipating [Formula: see text]-differential privacy that is tailored to the dynamic pricing problem. Our policy achieves both the privacy guarantee and the performance guarantee in terms of regret. Roughly speaking, for d-dimensional personalized information, our algorithm achieves the expected regret at the order of [Formula: see text] when the customers’ information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to [Formula: see text]. This paper was accepted by J. George Shanthikumar, big data analytics.

scholarly journals Privacy-Preserving Pandemic Monitoring

2021 ◽  
pp. 194-206
Author(s):  
Thu Yein Win ◽  
Hugo Tianfield
Keyword(s):  
Public Health ◽  
Data Sharing ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Patient Data ◽  
Future Research ◽  
Health Safety ◽  
Patient Data Privacy ◽  
Significant Challenge ◽  
The World

The recent COVID-19 pandemic has presented a significant challenge for health organisations around the world in providing treatment and ensuring public health safety. While this has highlighted the importance of data sharing amongst them, it has also highlighted the importance of ensuring patient data privacy in doing so. This chapter explores the different techniques which facilitate this, along with their overall implementations. It first provides an overview of pandemic monitoring and the privacy implications associated with it. It then explores the different privacy-preserving approaches that have been used in existing research. It also explores the strengths as well as their limitations, along with possible areas for future research.

scholarly journals Privacy in Cloud-Based Computing

2020 ◽  
pp. 239-252
Author(s):  
Monjur Ahmed ◽  
Nurul I. Sarkar
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Data Privacy ◽  
Fog Computing ◽  
Edge Computing ◽  
Third Party ◽  
Research Topics ◽  
Sensitive Data ◽  
Privacy Issues ◽  
Third Party Ownership

Cloud computing, internet of things (IoT), edge computing, and fog computing are gaining attention as emerging research topics and computing approaches in recent years. These computing approaches are rather conceptual and contextual strategies rather than being computing technologies themselves, and in practice, they often overlap. For example, an IoT architecture may incorporate cloud computing and fog computing. Cloud computing is a significant concept in contemporary computing and being adopted in almost every means of computing. All computing architectures incorporating cloud computing are termed as cloud-based computing (CbC) in general. However, cloud computing itself is the basis of CbC because it significantly depends on resources that are remote, and the remote resources are often under third-party ownership where the privacy of sensitive data is a big concern. This chapter investigates various privacy issues associated with CbC. The data privacy issues and possible solutions within the context of cloud computing, IoT, edge computing, and fog computing are also explored.

Privacy Preserving Classification of Biomedical Data With Secure Removing of Duplicate Records

2018 ◽  
Vol 8 (3) ◽  
pp. 41-58
Author(s):  
Boudheb Tarik ◽  
Elberrichi Zakaria
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Third Party ◽  
Distributed Data ◽  
Biomedical Data ◽  
Collaborative Models ◽  
Highly Sensitive ◽  
Complete Access

Classifying data is to automatically assign predefined classes to data. It is one of the main applications of data mining. Having complete access to all data is critical for building accurate models. Data can be highly sensitive, such as biomedical data, which cannot be disclosed or shared with third party, because it can harm individuals and organizations. The challenge is how to preserve privacy and usefulness of data. Privacy preserving classification addresses this problem. Collaborative models are constructed over networks without violating the data owners' privacy. In this article, the authors address two problems: privacy records deduplication of the same records and privacy-preserving classification. They propose a randomized hash technic for deduplication and an enhanced privacy preserving classification of biomedical data over horizontally distributed data based on two homomorphic encryptions. No private, intermediate or final results are disclosed. Experimentations show that their solution is efficient and secure without loss of accuracy.

scholarly journals Privacy-Preserving K-Nearest Neighbors Training over Blockchain-Based Encrypted Health Data

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2096
Author(s):  
Rakib Ul Haque ◽  
A S M Touhidul Hasan ◽  
Qingshan Jiang ◽  
Qiang Qu
Keyword(s):  
Data Privacy ◽  
Building Blocks ◽  
Privacy Preserving ◽  
Training Data ◽  
Third Party ◽  
Supervised Machine Learning ◽  
Primary Concern ◽  
Privacy Issue ◽  
Trusted Third Party ◽  
Blockchain Technology

Numerous works focus on the data privacy issue of the Internet of Things (IoT) when training a supervised Machine Learning (ML) classifier. Most of the existing solutions assume that the classifier’s training data can be obtained securely from different IoT data providers. The primary concern is data privacy when training a K-Nearest Neighbour (K-NN) classifier with IoT data from various entities. This paper proposes secure K-NN, which provides a privacy-preserving K-NN training over IoT data. It employs Blockchain technology with a partial homomorphic cryptosystem (PHC) known as Paillier in order to protect all participants (i.e., IoT data analyst C and IoT data provider P) data privacy. When C analyzes the IoT data of P, both participants’ privacy issue arises and requires a trusted third party. To protect each candidate’s privacy and remove the dependency on a third-party, we assemble secure building blocks in secure K-NN based on Blockchain technology. Firstly, a protected data-sharing platform is developed among various P, where encrypted IoT data is registered on a shared ledger. Secondly, the secure polynomial operation (SPO), secure biasing operations (SBO), and secure comparison (SC) are designed using the homomorphic property of Paillier. It shows that secure K-NN does not need any trusted third-party at the time of interaction, and rigorous security analysis demonstrates that secure K-NN protects sensitive data privacy for each P and C. The secure K-NN achieved 97.84%, 82.33%, and 76.33% precisions on BCWD, HDD, and DD datasets. The performance of secure K-NN is precisely similar to the general K-NN and outperforms all the previous state of art methods.

Combination of Access Control and De-Identification for Privacy Preserving in Big Data

2016 ◽  
Vol 10 (1) ◽  
pp. 1-27 ◽  
Author(s):  
Amine Rahmani ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou
Keyword(s):  
Big Data ◽  
Access Control ◽  
Data Privacy ◽  
Scientific Community ◽  
High Efficiency ◽  
Personal Information ◽  
Privacy Preserving ◽  
Big Data Privacy ◽  
Basic Concepts ◽  
Identification Techniques

Despite of its emergence and advantages in various domains, big data still suffers from major disadvantages. Timeless, scalability, and privacy are the main problems that hinder the advance of big data. Privacy preserving has become a wide search era within the scientific community. This paper covers the problem of privacy preserving over big data by combining both access control and data de-identification techniques in order to provide a powerful system. The aim of this system is to carry on all big data properties (volume, variety, velocity, veracity, and value) to ensure protection of users' identities. After many experiments and tests, our system shows high efficiency on detecting and hiding personal information while maintaining the utility of useful data. The remainder of this report is addressed in the presentation of some known works over a privacy preserving domain, the introduction of some basic concepts that are used to build our approach, the presentation of our system, and finally the display and discussion of the main results of our experiments.

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