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Low Latency Fog-Centric Deduplication Approach to Reduce IoT Healthcare Data Redundancy

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Abstract

IoT-based medical apparatuses observe the health condition of the patients and upload them to the cloud servers now and then. The increasing popularity and its real-time applications have made IoT healthcare monitoring as a promising technology. However, the redundant healthcare data produced by the IoT medical devices are exponentially growing and reduce the storage efficiency. Executing a deduplication algorithm, identifying the redundant data, and stopping them from entering into the cloud servers helps in improving the storage efficiency. Performing deduplication and stopping redundant data from entering the cloud will improve storage efficiency. However, in existing approaches, the communication overhead is heavy. Traditional hash-based and convergent key-based deduplication approaches use the entire hash values of the data chunks to perform deduplication as well as encryption. It makes the deduplication system vulnerable to confirmation of file attacks (CFA). In addition, convergent key deduplication uses an external key-server or third party to generate keys for users to encrypt their data. Incorporating and trusting external parties increase the security vulnerabilities. In addition, while performing deduplication, existing approaches face a high false-positive error on the hash table. To address these challenges and to improve the performance of the deduplication algorithm, a fog-centric approach is proposed. Instead of sending an entire hash value (α) of the data blocks to the cloud storage, partial hash values (Pαv) are shared with the nearby fog nodes. Each fog node in the fog layer maintains a Scalable Bloom Hash Table (SBHT) to store the partial hash values. Also, SBHT helps in performing inline deduplication. The hash bits of the Pαv are derived and stored in the Scalable Bloomier Hash Table (SBHT), maintained in the fog node to perform inline deduplication. Once the data are proved to be non-redundant, the edge node (client) encrypts the data chunk using an asymmetric Cramer Shoup (CS) cryptosystem and uploads to the cloud storage. The proposed method reduces the communication overhead by 65–70% and improves the security against CFA. In addition, the proposed SBHT reduces false-positive errors and improves storage efficiency, and makes it practical to implement in the real-time IoT healthcare environment.

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Data Availability

The data that support the findings of this study are openly available in UCI repository at http://archive.ics.uci.edu/ml/datasets/mhealth+dataset, [28].

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Acknowledgements

The authors of this research paper are highly thankful to the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India for financial support. Also, the authors would like to thank the reviewers in advance for their valuable comments and suggestions.

Funding

The work of this paper is financially supported by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India. The Grant Number is ECR/2016/000546.

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Authors and Affiliations

  1. School of Computer Science and Engineering, VIT-AP University, Amaravati, Andra Pradesh, India

    Mohamed Sirajudeen Yoosuf

  2. Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, Kancheepuram, Tamil Nadu, India

    R. Anitha

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  2. R. Anitha
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Correspondence to Mohamed Sirajudeen Yoosuf.

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Yoosuf, M.S., Anitha, R. Low Latency Fog-Centric Deduplication Approach to Reduce IoT Healthcare Data Redundancy. Wireless Pers Commun 126, 421–443 (2022). https://doi.org/10.1007/s11277-022-09752-5

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