scholarly journals Privacy-Preserving and Lightweight Selective Aggregation with Fault-Tolerance for Edge Computing-Enhanced IoT

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5369
Author(s):  
Qiannan Wang ◽  
Haibing Mu
Keyword(s):  
Fault Tolerance ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Fault Tolerant ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Edge Computing ◽  
Communication Overhead ◽  
Time Data ◽  
Selective Aggregation

Edge computing has been introduced to the Internet of Things (IoT) to meet the requirements of IoT applications. At the same time, data aggregation is widely used in data processing to reduce the communication overhead and energy consumption in IoT. Most existing schemes aggregate the overall data without filtering. In addition, aggregation schemes also face huge challenges, such as the privacy of the individual IoT device’s data or the fault-tolerant and lightweight requirements of the schemes. In this paper, we present a privacy-preserving and lightweight selective aggregation scheme with fault tolerance (PLSA-FT) for edge computing-enhanced IoT. In PLSA-FT, selective aggregation can be achieved by constructing Boolean responses and numerical responses according to specific query conditions of the cloud center. Furthermore, we modified the basic Paillier homomorphic encryption to guarantee data privacy and support fault tolerance of IoT devices’ malfunctions. An online/offline signature mechanism is utilized to reduce computation costs. The system characteristic analyses prove that the PLSA-FT scheme achieves confidentiality, privacy preservation, source authentication, integrity verification, fault tolerance, and dynamic membership management. Moreover, performance evaluation results show that PLSA-FT is lightweight with low computation costs and communication overheads.

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 EPPDA: An Efficient and Privacy-Preserving Data Aggregation Scheme with Authentication and Authorization for IoT-Based Healthcare

2021 ◽  
Author(s):  
Faris. A. Almalki ◽  
Ben othman Soufiene
Keyword(s):  
Data Aggregation ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Communication Overhead ◽  
Range Data ◽  
Aggregation Techniques ◽  
Aggregation Scheme ◽  
Iot Devices

Abstract Internet of Things (IoT) connects various kinds of intelligent objects and devices using the internet to collect and exchange data. Nowadays, The IoT is used in diverse application domains, including the healthcare. In the healthcare domain, the IoT devices can collects patient data, and its forwards the data to the healthcare professionals can view it. The IoT devices are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range, data aggregation techniques are used to reduce the communication overhead. However, in healthcare system using IoT, the heterogeneity of technologies, the large number of devices and systems, and the different types of users and roles create important challenges in terms of security. For that, the security and privacy aggregation of health data are very important aspects. In this paper, we propose a novel secure data aggregation scheme based on homomorphic primitives in IoT based healthcare systems, called “An Efficient and Privacy-Preserving Data Aggregation Scheme with authentication for IoT-Based Healthcare applications” (EPPDA). EPPDA is based the Verification and Authorization phase to verifying the legitimacy of the nodes wants to join the process of aggregation. EPPDA uses additive homomorphic encryption to protect data privacy and combines it with homomorphic MAC to check the data integrity. The security analysis and experimental results show that our proposed scheme guarantees data privacy, messages authenticity, and integrity, with lightweight communication overhead and computation.

Privacy Preserving Big Data Publishing

2018 ◽  
pp. 47-70 ◽  
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 Query Privacy Preserving for Data Aggregation in Wireless Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
X. Liu ◽  
X. Zhang ◽  
J. Yu ◽  
C. Fu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Sensor Nodes ◽  
Experimental Simulation ◽  
Wireless Sensor ◽  
Communication Overhead

Wireless Sensor Networks (WSNs) are increasingly involved in many applications. However, communication overhead and energy efficiency of sensor nodes are the major concerns in WSNs. In addition, the broadcast communication mode of WSNs makes the network vulnerable to privacy disclosure when the sensor nodes are subject to malicious behaviours. Based on the abovementioned issues, we present a Queries Privacy Preserving mechanism for Data Aggregation (QPPDA) which may reduce energy consumption by allowing multiple queries to be aggregated into a single packet and preserve data privacy effectively by employing a privacy homomorphic encryption scheme. The performance evaluations obtained from the theoretical analysis and the experimental simulation show that our mechanism can reduce the communication overhead of the network and protect the private data from being compromised.

scholarly journals Fault-Tolerant Privacy-Preserving Data Aggregation for Smart Grid

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Huadong Liu ◽  
Tianlong Gu ◽  
Yining Liu ◽  
Jingcheng Song ◽  
Zhixin Zeng
Keyword(s):  
Fault Tolerance ◽  
Secret Sharing ◽  
Data Aggregation ◽  
Smart Grids ◽  
Data Privacy ◽  
High Efficiency ◽  
Differential Privacy ◽  
Fault Tolerant ◽  
Privacy Preserving ◽  
Data Usability

In smart grids (SG), data aggregation is widely used to strike a balance between data usability and privacy protection. The fault tolerance is an important requirement to improve the robustness of data aggregation protocols, which enables normal execution of the protocols even with failures on some entities. However, to achieve fault tolerance, most schemes either sacrifice the aggregation accuracy due to the use of differential privacy or substitution strategy or need to rely on an online trusted entity to manage all user blinding factors. In this paper, a (k,n) threshold privacy-preserving data aggregation scheme named (k,n)-PDA is proposed, which reconciles data usability and data privacy through the BGN cryptosystem and achieves fault tolerance with accurate aggregation using Shamir’s secret sharing without any online trusted entity. Besides, our scheme supports the efficient changing of users’ membership. Specifically, the dynamic secrete key is distributed to n smart meters (SMs) through the threshold secret sharing algorithm. When k or more meters participate in the aggregation, the data service center (DSC) can reconstruct the key to compute the aggregate results, and less than k SMs cannot recover the key. Thus, our solution still works functionally even if up to n−k SMs fail; also, it resists attacks from the collusion of less than k SMs. Moreover, system and performance analyses demonstrate that our scheme achieves privacy, fault tolerance, and membership dynamics with high efficiency.

Power Consumption Data Privacy-preserving Scheme Based on Improved Multi-key Fully Homomorphic Encryption

2021 ◽  
Vol 14 ◽  
Author(s):  
Yuancheng Li ◽  
Jiawen Yu
Keyword(s):  
Power Consumption ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Homomorphic Encryption ◽  
Computational Cost ◽  
Privacy Preserving ◽  
Secret Key ◽  
Multiple Data ◽  
Consumption Data ◽  
Secret Keys

Background: In the power Internet of Things (IoT), power consumption data faces the risk of privacy leakage. Traditional privacy-preserving schemes cannot ensure data privacy on the system, as the secret key pairs shall be shared between all the interior nodes once leaked. In addition, the general schemes only support summation algorithms, resulting in a lack of extensibility. Objective: To preserve the privacy of power consumption data, ensure the privacy of secret keys, and support multiple data processing methods, we propose an improved power consumption data privacy-preserving scheme. Method: Firstly, we have established a power IoT architecture based on edge computing. Then the data is encrypted with the multi-key fully homomorphic algorithm to realize the operation of ciphertext, without the restrictions of calculation type. Through the improved decryption algorithm, ciphertext that can be separately decrypted in cloud nodes is generated, which contributes to reducing communication costs and preventing data leakage. Results: The experimental results show that our scheme is more efficient than traditional schemes in privacy preservation. According to the variance calculation result, the proposed scheme has reached the application standard in terms of computational cost and is feasible for practical operation. Discussion: In the future, we plan to adopt a secure multi-party computation based scheme so that data can be managed locally with homomorphic encryption, so as to ensure data privacy.

Integrity and Privacy Preserving Data Aggregation Algorithm for WSNs

2014 ◽  
Vol 721 ◽  
pp. 732-735
Author(s):  
Hua Zhang
Keyword(s):  
Data Aggregation ◽  
Data Privacy ◽  
Chinese Remainder Theorem ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Message Authentication ◽  
Authentication Codes ◽  
Integrity Checking ◽  
Data Freshness ◽  
Simulation Results

This paper proposed an integrity and privacy preserving data aggregation algorithm for WSNs, which is called IPPDA. First, it attached a group of congruent numbers to the sensing data in order to execute integrity checking operated by sink node using Chinese remainder theorem (CRT); then it computed the hash function-based message authentication codes with time and key as the parameters to satisfy data freshness; finally, it adopted a homomorphic encryption scheme to provide privacy preserving. The simulation results show that IPPDA can effectively preserve data privacy, check data integrity, satisfy data freshness, and get accurate data aggregation results while having less computation and communication cost than iCPDA and iPDA.

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.

scholarly journals A Secure Privacy-Preserving Data Aggregation Model in Wearable Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Changlun Zhang ◽  
Chao Li ◽  
Jian Zhang
Keyword(s):  
Data Aggregation ◽  
Privacy Preservation ◽  
Rapid Development ◽  
Cluster Head ◽  
Privacy Preserving ◽  
Base Station ◽  
Sensor Nodes ◽  
Mixed Data ◽  
Communication Overhead ◽  
Aggregation Model

With the rapid development and widespread use of wearable wireless sensors, data aggregation technique becomes one of the most important research areas. However, the sensitive data collected by sensor nodes may be leaked at the intermediate aggregator nodes. So, privacy preservation is becoming an increasingly important issue in security data aggregation. In this paper, we propose a security privacy-preserving data aggregation model, which adopts a mixed data aggregation structure. Data integrity is verified both at cluster head and at base station. Some nodes adopt slicing technology to avoid the leak of data at the cluster head in inner-cluster. Furthermore, a mechanism is given to locate the compromised nodes. The analysis shows that the model is robust to many attacks and has a lower communication overhead.

scholarly journals FPETD: Fault-Tolerant and Privacy-Preserving Electricity Theft Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Siliang Dong ◽  
Zhixin Zeng ◽  
Yining Liu
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
Fault Tolerant ◽  
Privacy Preserving ◽  
User Privacy ◽  
Accuracy Rate ◽  
Electricity Theft ◽  
User Data ◽  
Dimensionality Reduction Method ◽  
Better Than

Electricity theft occurs from time to time in the smart grid, which can cause great losses to the power supplier, so it is necessary to prevent the occurrence of electricity theft. Using machine learning as an electricity theft detection tool can quickly lock participants suspected of electricity theft; however, directly publishing user data to the detector for machine learning-based detection may expose user privacy. In this paper, we propose a real-time fault-tolerant and privacy-preserving electricity theft detection (FPETD) scheme that combines n -source anonymity and a convolutional neural network (CNN). In our scheme, we designed a fault-tolerant raw data collection protocol to collect electricity data and cut off the correspondence between users and their data, thereby ensuring the fault tolerance and data privacy during the electricity theft detection process. Experiments have proven that our dimensionality reduction method makes our model have an accuracy rate of 92.86% for detecting electricity theft, which is much better than others.

Sign in / Sign up

Export Citation Format

Share Document