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 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 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

scholarly journals Secure Data Aggregation Scheme for IOT Applications in Cloud

2020 ◽  
Vol 9 (4S2) ◽  
pp. 70-76
Keyword(s):  
Cloud Computing ◽  
Data Hiding ◽  
Data Aggregation ◽  
Dynamic Network ◽  
Security And Privacy ◽  
Iot Applications ◽  
P Gene ◽  
Private Data ◽  
Aggregation Scheme ◽  
And Storage

While Internet of Things (IoT) technology comprises of nodes that are self-configuring and intelligent which are interconnected in a dynamic network, utilization of shared resources has been revolutionized by the cloud computing effectively reducing the cost overheadamong the cloud users.The major concerns of IoT infrastructure are reliability, performance, security and privacy. Cloud computing is popular for its unlimited storage and processing power. Cloud computing is much more matured with the capability to resolve most of the issues in IoT technology. A suitable way to address most of the issues in IoT technology is by integrating IoTparadigm into the Cloud technology.In this regard, we propose a methodology of applying our EPAS scheme for IoT applications. In our previous work[2] , we have proposed an Enhanced Privacy preserving gene based data Aggregation Scheme (EPAS) for private data transmission and storage by utilizing Enhanced P-Gene erasable data hiding approach. Enhanced P-Gene scheme ensures secure transmission and storage of private data by relying on a data aggregation scheme fully dependent on erasable data hiding technique. In the current work we analyse the applicability of the EPAS scheme for IoT applications. Experimental results show the suitability of the proposed scheme for application involving numeric data and also demonstrates performance improvement with existing proposals for data aggregation in cloud.

FPDA: Fault-Tolerant and Privacy-Enhanced Data Aggregation Scheme in Fog-Assisted Smart Grid

2021 ◽  
pp. 1-1
Author(s):  
Liqiang Wu ◽  
Ming Xu ◽  
Shaojing Fu ◽  
Yuchuan Luo ◽  
Yuechuan Wei
Keyword(s):  
Smart Grid ◽  
Data Aggregation ◽  
Fault Tolerant ◽  
Aggregation Scheme

scholarly journals Two Secure Privacy-Preserving Data Aggregation Schemes for IoT

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yuwen Pu ◽  
Jin Luo ◽  
Chunqiang Hu ◽  
Jiguo Yu ◽  
Ruifeng Zhao ◽  
...  
Keyword(s):  
Data Aggregation ◽  
Secure Communication ◽  
Homomorphic Encryption ◽  
Computational Cost ◽  
Health Condition ◽  
Security And Privacy ◽  
Actual Data ◽  
Smart Healthcare ◽  
Private Data ◽  
Noise Data

As the next generation of information and communication infrastructure, Internet of Things (IoT) enables many advanced applications such as smart healthcare, smart grid, smart home, and so on, which provide the most flexibility and convenience in our daily life. However, pervasive security and privacy issues are also increasing in IoT. For instance, an attacker can get health condition of a patient via analyzing real-time records in a smart healthcare application. Therefore, it is very important for users to protect their private data. In this paper, we present two efficient data aggregation schemes to preserve private data of customers. In the first scheme, each IoT device slices its actual data randomly, keeps one piece to itself, and sends the remaining pieces to other devices which are in the same group via symmetric encryption. Then, each IoT device adds the received pieces and the held piece together to get an immediate result, which is sent to the aggregator after the computation. Moreover, homomorphic encryption and AES encryption are employed to guarantee secure communication. In the second scheme, the slicing strategy is also employed. Noise data are introduced to prevent the exchanged actual data of devices from disclosure when the devices blend data each other. AES encryption is also employed to guarantee secure communication between devices and aggregator, compared to homomorphic encryption, which has significantly less computational cost. Analysis shows that integrity and confidentiality of IoT devices’ data can be guaranteed in our schemes. Both schemes can resist external attack, internal attack, colluding attack, and so on.

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.

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.

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