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 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 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-Preserving Data Aggregation Framework for Mobile Service Based Multiuser Collaboration

2019 ◽  
Vol 17 (4) ◽  
pp. 450-460
Author(s):  
Hai Liu ◽  
Zhenqiang Wu ◽  
Changgen Peng ◽  
Feng Tian ◽  
Laifeng Lu
Keyword(s):  
Nash Equilibrium ◽  
Theoretical Analysis ◽  
Expected Utility ◽  
Data Aggregation ◽  
Experimental Evaluation ◽  
Differential Privacy ◽  
Privacy Preserving ◽  
Mobile Service ◽  
Utility Factor ◽  
Privacy Budget

Considering the untrusted server, differential privacy and local differential privacy has been used for privacy-preserving in data aggregation. Through our analysis, differential privacy and local differential privacy cannot achieve Nash equilibrium between privacy and utility for mobile service based multiuser collaboration, which is multiuser negotiating a desired privacy budget in a collaborative manner for privacy-preserving. To this end, we proposed a Privacy-Preserving Data Aggregation Framework (PPDAF) that reached Nash equilibrium between privacy and utility. Firstly, we presented an adaptive Gaussian mechanism satisfying Nash equilibrium between privacy and utility by multiplying expected utility factor with conditional filtering noise under expected privacy budget. Secondly, we constructed PPDAF using adaptive Gaussian mechanism based on negotiating privacy budget with heuristic obfuscation. Finally, our theoretical analysis and experimental evaluation showed that the PPDAF could achieve Nash equilibrium between privacy and utility. Furthermore, this framework can be extended to engineering instances in a data aggregation setting

PPM-HDA: Privacy-Preserving and Multifunctional Health Data Aggregation With Fault Tolerance

2016 ◽  
Vol 11 (9) ◽  
pp. 1940-1955 ◽  
Author(s):  
Song Han ◽  
Shuai Zhao ◽  
Qinghua Li ◽  
Chun-Hua Ju ◽  
Wanlei Zhou
Keyword(s):  
Fault Tolerance ◽  
Data Aggregation ◽  
Privacy Preserving ◽  
Health Data
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