scholarly journals Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees

2021 ◽  
Author(s):  
Hao Ren ◽  
Hongwei Li ◽  
Xiaohui Liang ◽  
Shibo He ◽  
Yuanshun Dai ◽  
...  
Keyword(s):  
Data Aggregation ◽  
Data Privacy ◽  
Differential Privacy ◽  
Weighted Average ◽  
Health Data ◽  
Communication Overhead ◽  
Aggregated Data ◽  
Body Area Sensor Networks ◽  
Aggregation Scheme ◽  
Cloud Servers

With the rapid growth of the health data scale, the limited storage and computation resources of wireless body area sensor networks (WBANs) is becoming a barrier to their development. Therefore, outsourcing the encrypted health data to the cloud has been an appealing strategy. However, date aggregation will become difficult. Some recently-proposed schemes try to address this problem. However, there are still some functions and privacy issues that are not discussed. In this paper, we propose a privacy-enhanced and multifunctional health data aggregation scheme (PMHA-DP) under differential privacy. Specifically, we achieve a new aggregation function, weighted average (WAAS), and design a privacy-enhanced aggregation scheme (PAAS) to protect the aggregated data from cloud servers. Besides, a histogram aggregation scheme with high accuracy is proposed. PMHA-DP supports fault tolerance while preserving data privacy. The performance evaluation shows that the proposal leads to less communication overhead than the existing one.

scholarly journals Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees

2021 ◽  
Author(s):  
Hao Ren ◽  
Hongwei Li ◽  
Xiaohui Liang ◽  
Shibo He ◽  
Yuanshun Dai ◽  
...  
Keyword(s):  
Data Aggregation ◽  
Data Privacy ◽  
Differential Privacy ◽  
Weighted Average ◽  
Health Data ◽  
Communication Overhead ◽  
Aggregated Data ◽  
Body Area Sensor Networks ◽  
Aggregation Scheme ◽  
Cloud Servers

With the rapid growth of the health data scale, the limited storage and computation resources of wireless body area sensor networks (WBANs) is becoming a barrier to their development. Therefore, outsourcing the encrypted health data to the cloud has been an appealing strategy. However, date aggregation will become difficult. Some recently-proposed schemes try to address this problem. However, there are still some functions and privacy issues that are not discussed. In this paper, we propose a privacy-enhanced and multifunctional health data aggregation scheme (PMHA-DP) under differential privacy. Specifically, we achieve a new aggregation function, weighted average (WAAS), and design a privacy-enhanced aggregation scheme (PAAS) to protect the aggregated data from cloud servers. Besides, a histogram aggregation scheme with high accuracy is proposed. PMHA-DP supports fault tolerance while preserving data privacy. The performance evaluation shows that the proposal leads to less communication overhead than the existing one.

scholarly journals A novel fault-tolerant privacy-preserving cloud-based data aggregation scheme for lightweight health data

2021 ◽  
Vol 18 (6) ◽  
pp. 7539-7560
Author(s):  
Fawza A. Al-Zumia ◽  
◽  
Yuan Tian ◽  
Mznah Al-Rodhaan ◽  
Keyword(s):  
Data Aggregation ◽  
Differential Privacy ◽  
Fault Tolerant ◽  
Computational Cost ◽  
Health Data ◽  
Security And Privacy ◽  
Health Networks ◽  
Aggregated Data ◽  
Medical Health Care ◽  
Aggregation Scheme

<abstract> <p>Mobile health networks (MHNWs) have facilitated instant medical health care and remote health monitoring for patients. Currently, a vast amount of health data needs to be quickly collected, processed and analyzed. The main barrier to doing so is the limited amount of the computational storage resources that are required for MHNWs. Therefore, health data must be outsourced to the cloud. Although the cloud has the benefits of powerful computation capabilities and intensive storage resources, security and privacy concerns exist. Therefore, our study examines how to collect and aggregate these health data securely and efficiently, with a focus on the theoretical importance and application potential of the aggregated data. In this work, we propose a novel design for a private and fault-tolerant cloud-based data aggregation scheme. Our design is based on a future ciphertext mechanism for improving the fault tolerance capabilities of MHNWs. Our scheme is privatized via differential privacy, which is achieved by encrypting noisy health data and enabling the cloud to obtain the results of only the noisy sum. Our scheme is efficient, reliable and secure and combines different approaches and algorithms to improve the security and efficiency of the system. Our proposed scheme is evaluated with an extensive simulation study, and the simulation results show that it is efficient and reliable. The computational cost of our scheme is significantly less than that of the related scheme. The aggregation error is minimized from ${\rm{O}}\left( {\sqrt {{\bf{w + 1}}} } \right)$ in the related scheme to O(1) in our scheme.</p> </abstract>

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 Privacy-Preserving Multidimensional Data Aggregation Scheme for Smart Grid

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yousheng Zhou ◽  
Xinyun Chen ◽  
Meihuan Chen
Keyword(s):  
Smart Grid ◽  
Data Aggregation ◽  
Data Privacy ◽  
Random Oracle Model ◽  
Privacy Preserving ◽  
Multidimensional Data ◽  
Communication Overhead ◽  
Security Threat ◽  
Grid Data ◽  
Aggregation Scheme

In a smart grid, data aggregation is a common method to evaluate regional power consumption. Data leakage in the process of data transmission poses a security threat to the privacy of users. Many existing data aggregation schemes can only aggregate one-dimensional data; however, it is necessary to aggregate multidimensional data in practical smart grid applications. Therefore, this paper proposes a privacy-preserving multidimensional data aggregation scheme, which can aggregate multidimensional data and protect the individual user’s identity and data privacy. The security of the proposed scheme is proved under the random oracle model. The simulation results show that the proposed scheme has great advantages in computing overhead, and the communication overhead also meets the requirements of the smart grid.

Data Aggregation with End-to-End Security for Wireless Sensor Networks

2012 ◽  
Vol 490-495 ◽  
pp. 383-386
Author(s):  
Jiang Hong Guo ◽  
Jian Qiang Wu ◽  
Xi Hong Wu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Data Privacy ◽  
Identity Authentication ◽  
Wireless Sensor ◽  
Communication Overhead ◽  
Remote Server ◽  
Aggregation Scheme ◽  
End To End

Secure end-to-end data transmission is an important method to protect the data privacy in wireless sensor networks. Authors proposed a data aggregation scheme with end-to-end security for wireless sensor networks. The plaintext of sensor readings only appeared in source node and remote server, the aggregators completed the data integrity verification, sender identity authentication and data aggregation without the plaintext. Analysis and simulation show that our scheme has higher security in terms of resilient against malicious attacks and reduces the communication overhead effectively

scholarly journals Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees

Sensors ◽  
2016 ◽  
Vol 16 (9) ◽  
pp. 1463 ◽  
Author(s):  
Hao Ren ◽  
Hongwei Li ◽  
Xiaohui Liang ◽  
Shibo He ◽  
Yuanshun Dai ◽  
...  
Keyword(s):  
Data Aggregation ◽  
Differential Privacy ◽  
Health Data

Secure Multi-Subinterval Data Aggregation Scheme with Interval Privacy Preservation for Vehicle Sensing Systems

2021 ◽  
pp. 2150286
Author(s):  
Peng Hu ◽  
Yongli Wang ◽  
Ahmadreza Vajdi ◽  
Bei Gong ◽  
Yongjian Wang
Keyword(s):  
Data Aggregation ◽  
Privacy Preservation ◽  
Homomorphic Encryption ◽  
Security Analysis ◽  
Security And Privacy ◽  
Communication Overhead ◽  
Network Resources ◽  
Sensing Systems ◽  
Aggregation Scheme ◽  
And Performance

Road side units (RSUs) can act as fog nodes to perform data aggregation at the edge of network, which can reduce communication overhead and improve the utilization of network resources. However, because the RSU is public infrastructure, this feature may bring data security and privacy risks in data aggregation. In this paper, we propose a secure multi-subinterval data aggregation scheme, named SMDA, with interval privacy preservation for vehicle sensing systems. Specifically, our scheme combines the [Formula: see text] encoding theory and proxy re-encryption to protect interval privacy, this can ensure that the interval information is only known by the data center, and the RSU can classify the encrypted data without knowing the plaintext of the data and interval information. Meanwhile, our scheme employs the Paillier homomorphic encryption to accomplish data aggregation at the RSU, and the Identity-based batch authentication technology to solve authentication and data integrity. Finally, the security analysis and performance evaluations illustrate the safety and efficiency of our scheme.

Covering Set-Based Inner-Cluster Data Aggregation Scheme for Wireless Sensor Networks

2013 ◽  
Vol 321-324 ◽  
pp. 592-595
Author(s):  
Jiang Hong Guo ◽  
Yu Dong Luo
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Reference Point ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Communication Overhead ◽  
Cluster Data ◽  
Aggregation Scheme ◽  
Covering Set

For reducing the communication overhead of data aggregation, authors proposed an covering set-based inner-cluster data aggregation scheme for wireless sensor networks. The network is clustered upon deployment; nodes achieve the inner-cluster neighbors’ identifiers by information exchanging and transmit the list of inner-cluster neighbors to the cluster head to help the cluster head deducing the inner-cluster covering set. Each node selects a neighbor form inner-cluster covering set as reference point and keeps silent if it has the same reading with reference point. Simulation shows that the proposed scheme lowers the communication overhead of data aggregation effectively.

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