A New Weight and Sensitivity Based Variable Maximum Distance to Average Vector Algorithm for Wearable Sensor Data Privacy Protection

Abstract

The problem of privacy protection of wearable devices when publishing data can be solved based on the variable-maximum distance average vector. This paper proposes a new weight and sensitivity based variable maximum distance average vector (WSV-MDAV) method aiming to solve the problems that may be contained in the existing privacy protection algorithm. The proposed approach considers the difference of the importance among all the identifiers by setting corresponding weight coefficient W . Given a specific weight to each attribute in the table, we can subsequently get a distance metric based on weight. Similarly, the different sensitivity constraint S for different sensitive attributes is also available for our proposed method. Using the WSV-MDAV algorithm we propose a new privacy protection model for the data publishing of wearable device by introducing the concept of the differential privacy. The numerical results show that the proposed WSV-MDAV algorithm improves the privacy protection performance and reduces the information loss compared to the traditional method.INDEX TERMS Wearable device, privacy protection, micro aggregation, differential privacy.

A Systematic Literature Review on Wearable Health Data Publishing under Differential Privacy

“…They have focused mainly on effectiveness of publishing data, how efficiently data can be collected, reducing calculation and communication overhead, maintaining availability and reliability so that proposed solutions provide better accuracy as well as maintain the balance between utility and privacy. Differential privacy based schemes such as, MHDA ⊕ [42], Re-DPoctor [40], EDPDCS [43], WSV-MDAV [49], APDP [51] outperforms other existing methods. PMHA-DP [47] has less communication overhead than existing solutions.…”

“…Their experiment have resulted in successful privacy enhancement while minimizing overall privacy budget and eliminating background knowledge attack. Authors in [49] have identified and solved the issues of V-MDAV algorithm and then, have proposed a privacy protection model, based on an aggregation algorithm, named WSV-MDAV for wearable devices using DP. According to their experimental assessment, their technique have improved privacy protection performance and reduced data loss when compared to the traditional method.…”

“…The limitations of existing methodologies need to be addressed for a suitable, and more practical privacy preserved framework. [40], [39], [51], [49], [42], [43], [44], [45], [48], [46], [47], [52], [53], [54] w-day ε-DP [41] Real-time…”

“…Physiological category: For physiological data, the proposed DP solutions mainly suffer from scalability issues [52]. Many of the proposed differential privacy models are strict to static data publishing and confined to single dimension [49]. Moreover, many of the privacy protection schemes are just theoretical in nature [38,50].…”

Mitigating the Threat due to Data Deduplication Attacks in Cloud Migration using User Layer Authentication with Light Weight Cryptography

“…The combined efforts of authentication of healthcare-related data with IoT based control systems ensure a secure mechanism of preserving privacy and data security on the cloud by preventing malicious access [16]. Apart from this, the privacy protection of the wearable device of the IoT ecosystem is being studied by Zhang et al (2019), while publishing the data using distance vector mechanism [17]. With the balance between security and storage costs, the growing scale of digital medical data is being addressed through improved deduplication and ABE and minimized computing overhead.…”