scholarly journals A secure and efficient privacy-preserving data aggregation algorithm

2021 ◽  
Author(s):  
Hui Dou ◽  
Yuling Chen ◽  
Yixian Yang ◽  
Yangyang Long
Keyword(s):  
Data Aggregation ◽  
Privacy Protection ◽  
Data Privacy ◽  
Cluster Head ◽  
Privacy Preserving ◽  
Data Traffic ◽  
Life Data ◽  
Private Data ◽  
Save Energy ◽  
The Internet Of Things

AbstractAs a significant part of the Internet of things, wireless sensor networks (WSNs) is frequently implemented in our daily life. Data aggregation in WSNs can realize limited transmission and save energy. In the process of data aggregation, node data information is vulnerable to be eavesdropped and attacked. Therefore, it is of great significance to the research of data aggregation privacy protection in WSNs. We propose a secure and efficient privacy-preserving data aggregation algorithm (SECPDA) based on the original clustering privacy data aggregation algorithm. In this algorithm, we utilize SEP protocol to dynamically select cluster head nodes, introduce slicing idea for the private data slicing, and generate false information for interference. A comprehensive experimental evaluation is conducted to assess the data traffic and privacy protection performance. The results demonstrate that the proposed SECPDA algorithm can effectively reduce data traffic and further improve data privacy of nodes.

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.

Consensus-Based Data-Privacy Preserving Data Aggregation

2019 ◽  
Vol 64 (12) ◽  
pp. 5222-5229 ◽  
Author(s):  
Jianping He ◽  
Lin Cai ◽  
Peng Cheng ◽  
Jianping Pan ◽  
Ling Shi
Keyword(s):  
Data Aggregation ◽  
Data Privacy ◽  
Privacy Preserving

Privacy Protection in the Internet of Things Based on Cryptography

2015 ◽  
Vol 713-715 ◽  
pp. 2462-2466
Author(s):  
Xiu Rong Li ◽  
Shuang Zheng ◽  
Ya Li Liu
Keyword(s):  
Internet Of Things ◽  
Information Hiding ◽  
Privacy Protection ◽  
Data Privacy ◽  
Location Privacy ◽  
The Internet ◽  
Privacy Threats ◽  
Location Privacy Protection ◽  
The Internet Of Things

In this paper, we describe some privacy threats in the Internet of Things and some research works on privacy protection. We present a new scheme base on cryptosystem to protect privacy in the Internet of Things. The scheme includes location privacy protection, data privacy homomorphism mechanism and information hiding technology, and secure multi-party computation on data privacy.

A Privacy-Preserving Intra-Cluster Data Aggregation Method in Wireless Sensor Networks

2015 ◽  
Vol 719-720 ◽  
pp. 773-778
Author(s):  
Run Ze Wan ◽  
Xing Yan Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Privacy Protection ◽  
Privacy Preservation ◽  
Hierarchical Cluster ◽  
Wireless Sensor ◽  
Aggregation Method ◽  
Cluster Data ◽  
Private Data

The self-organizing nature of its architecture of wireless sensor networks (WSNs) introduce unique challenges for privacy preservation of data. This paper analyzes the cluster-based private data aggregation protocol (CPDA), and proposes a privacy protection protocol based on hierarchical cluster. We firstly reorganize nodes in cluster according to the logic structure of binary-tree in which each node transmits slice data instead of the full one. And then, we establish the hierarchical privacy tree to manage the group of keys, which purpose is privacy protection in case of data aggregation.

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 Privacy-Preserving Restricted Boltzmann Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Yu Li ◽  
Yuan Zhang ◽  
Yue Ji
Keyword(s):  
Data Privacy ◽  
Efficiency Analysis ◽  
Privacy Preserving ◽  
Restricted Boltzmann Machine ◽  
Distributed Data Mining ◽  
Distributed Data ◽  
Boltzmann Machine ◽  
Crucial Issue ◽  
Private Data ◽  
Technology Revolution

With the arrival of the big data era, it is predicted that distributed data mining will lead to an information technology revolution. To motivate different institutes to collaborate with each other, the crucial issue is to eliminate their concerns regarding data privacy. In this paper, we propose a privacy-preserving method for training a restricted boltzmann machine (RBM). The RBM can be got without revealing their private data to each other when using our privacy-preserving method. We provide a correctness and efficiency analysis of our algorithms. The comparative experiment shows that the accuracy is very close to the original RBM model.

scholarly journals Multiorder Fusion Data Privacy-Preserving Scheme for Wireless Sensor Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Mingshan Xie ◽  
Yong Bai ◽  
Mengxing Huang ◽  
Zhuhua Hu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Privacy Protection ◽  
Data Privacy ◽  
Large Scale ◽  
Privacy Preserving ◽  
Traffic Load ◽  
Wireless Sensor ◽  
Fusion Data ◽  
Data Privacy Protection

Privacy-preserving in wireless sensor networks is one of the key problems to be solved in practical applications. It is of great significance to solve the problem of data privacy protection for large-scale applications of wireless sensor networks. The characteristics of wireless sensor networks make data privacy protection technology face serious challenges. At present, the technology of data privacy protection in wireless sensor networks has become a hot research topic, mainly for data aggregation, data query, and access control of data privacy protection. In this paper, multiorder fusion data privacy-preserving scheme (MOFDAP) is proposed. Random interference code, random decomposition of function library, and cryptographic vector are introduced for our proposed scheme. In multiple stages and multiple aspects, the difficulty of cracking and crack costs are increased. The simulation results demonstrate that, compared with the typical Slice-Mix-AggRegaTe (SMART) algorithm, the algorithm proposed in this paper has a better data privacy-preserving ability when the traffic load is not very heavy.

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.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Faris A. Almalki ◽  
Ben Othman Soufiene
Keyword(s):  
Data Aggregation ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Data Integrity ◽  
Privacy Preserving ◽  
Healthcare Applications ◽  
Healthcare Data ◽  
Secure Data ◽  
Aggregation Scheme ◽  
Secure Data Aggregation

Nowadays, IoT technology is used in various application domains, including the healthcare, where sensors and IoT enabled medical devices exchange data without human interaction to securely transmit collected sensitive healthcare data towards healthcare professionals to be reviewed and take proper actions if needed. The IoT devices are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range. In healthcare applications, many miniaturized devices are exploited for healthcare data collection and transmission. Thus, there is a need for secure data aggregation while preserving the data integrity and privacy of the patient. For that, the security, privacy, and aggregation of health data are very important aspects to be considered. This paper proposes a novel secure data aggregation scheme called “An Efficient and Privacy-Preserving Data Aggregation Scheme with authentication for IoT-Based Healthcare applications” (EPPDA). EPPDA is based to verification and authorization phase to verify the legitimacy of the nodes that need to join the process of aggregation. EPPDA, also, uses additive homomorphic encryption to protect data privacy and combines it with homomorphic MAC to check the data integrity. The major advantage of homomorphic encryption is allowing complex mathematical operations to be performed on encrypted data without knowing the contents of the original plain data. The proposed system is developed using MySignals HW V2 platform. Security analysis and experimental results show that our proposed scheme guarantees data privacy, messages authenticity, and integrity, with lightweight communication overhead and computation.

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.

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