ESALP2: Efficient Signature Aggregation with Location Privacy Preservation in Wireless Body Area Networks

For Wireless Body Area Networks (WBANs), the security of sensitive data of patients is of the utmost importance, particularly in healthcare environments. This study presents a novel methodology for improving the efficacy of signature aggregation in a scenario involving doctors and patients while mitigating concerns about location privacy. Though there have been prior proposals for signature aggregation schemes, the proposed approach seeks to optimize the aggregation process within the considered scenario, thereby improving performance and reducing computational and communication burden. In addition, the proposed scheme integrates a resilient mechanism that safeguards the doctor’s location privacy by utilizing the Chinese Remainder Theorem (CRT). Advanced cryptographic algorithms and location-anonymization techniques are employed in the proposed method to safeguard the confidentiality of the doctors’ location. The security of the proposed scheme is formally analyzed using the Burrows-Abadi-Needham (BAN) logic and formally verified using the automated software validation tool, known as the Scyther tool, and an informal analysis of various security attributes confirms the security robustness of the proposed scheme. The efficacy is evaluated in comparison to analogous works utilizing the Cygwin software. The performance evaluation shows that the proposed scheme has lower communication costs as compared to existing competing schemes. Moreover, the serving ratio in the proposed scheme is high even if the number of patients is low for doctors.

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Publication Details

Journal
ACM Transactions on Sensor Networks
Published
2025-05-24
DOI
https://doi.org/10.1145/3737460
Citations
1
Primary Topic
Advanced Authentication Protocols Security
Type
article
Field-Weighted Citation Impact
1.49
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article

ESALP2: Efficient Signature Aggregation with Location Privacy Preservation in Wireless Body Area Networks

Ashok Kumar Das, Arun Sekar Rajasekaran, Azees Maria, Mohammed J. F. Alenazi et al.
1 citations
ACM Transactions on Sensor Networks
Advanced Authentication Protocols Security
1.49
article

ESALP2: Efficient Signature Aggregation with Location Privacy Preservation in Wireless Body Area Networks

Ashok Kumar Das, Arun Sekar Rajasekaran, Azees Maria, Mohammed J. F. Alenazi, Basker Palaniswamy
article en
1 citations

Abstract

For Wireless Body Area Networks (WBANs), the security of sensitive data of patients is of the utmost importance, particularly in healthcare environments. This study presents a novel methodology for improving the efficacy of signature aggregation in a scenario involving doctors and patients while mitigating concerns about location privacy. Though there have been prior proposals for signature aggregation schemes, the proposed approach seeks to optimize the aggregation process within the considered scenario, thereby improving performance and reducing computational and communication burden. In addition, the proposed scheme integrates a resilient mechanism that safeguards the doctor’s location privacy by utilizing the Chinese Remainder Theorem (CRT). Advanced cryptographic algorithms and location-anonymization techniques are employed in the proposed method to safeguard the confidentiality of the doctors’ location. The security of the proposed scheme is formally analyzed using the Burrows-Abadi-Needham (BAN) logic and formally verified using the automated software validation tool, known as the Scyther tool, and an informal analysis of various security attributes confirms the security robustness of the proposed scheme. The efficacy is evaluated in comparison to analogous works utilizing the Cygwin software. The performance evaluation shows that the proposed scheme has lower communication costs as compared to existing competing schemes. Moreover, the serving ratio in the proposed scheme is high even if the number of patients is low for doctors.

ACM Transactions on Sensor NetworksVol. 21(4)
International Institute of Information Technology (IN), Korea University (KR), University College Cork (IE), King Saud University (SA), SRM University (IN), VIT-AP University, International Institute of Information Technology, Hyderabad (IN)
Openalex Percentile: Top 17%
Advanced Authentication Protocols Security
1.49
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