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Technique and reference | Focus area(s) of the paper | Strengths | Weakness |
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PHDA [15] | (i) Priority-based health data aggregation. (ii) Paillier cryptographic technique | (i) Low energy consumption (ii) Ensure data privacy and integrity | (i) Asymmetric cryptosystem is computationally expensive |
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PPM-HAD [16] | (i) Privacy-preserving and multifunctional health data aggregation | (i) High fault tolerant (ii) Ensure data privacy and integrity | (i) Not verified in a real-life environment (ii) High traffic load |
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LSDA [17] | (i) Secure data aggregation in healthcare using IoT (ii) Homomorphic encryption and MAC. (iii) Packet checking at aggregator | (i) Evaluation results using an experimental network of medical sensors (ii) Robust communication (iii) Efficient communication between doctors and patients | (i) ECEG is power hungry cryptography (ii) Low fault tolerant (iii) Can be vulnerable to impersonation attack |
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RESDA [18] | (i) Secure data aggregation in healthcare using IoT (ii) Homomorphic encryption and MAC (iii) No packet checking at aggregator | (i) Evaluation results using an experimental network of medical sensors (ii) Providing strong privacy guarantees | (i) ECEG is power hungry cryptography (ii) Easy target for high-end attacks |
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ERCS [19] | (i) Trust-based communication scheme to ensure reliability and privacy of WBAN (ii) Cooperative communication approach | (i) Increases service delivery ratio, reliability, and trust with reduced average delay (ii) Guaranteeing the confidentiality of sensitive medical data | (i) High traffic load (ii) Not verified in a real-life environment (iii) Energy consumption |
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CBCSES [20] | (i) Efficient and provable secure scheme for IoT in Mobile health system (ii) Certificate-based combined signature, encryption and signcryption | (i) Securing the patients’ sensitive data (ii) Providing efficient performance in terms of energy consumption, frequency and cost. | (i) Not verified in a real-life environment (ii) Communication cost is high. (iii) Not considering heterogeneity of sensors |
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Proposed EPPDA | (i) Secure and energy-efficient data aggregation in healthcare using IoT with malicious node detection. (ii) Homomorphic encryption and MAC (iii) Packet checking at aggregator (iv) Priority-based health data aggregation | (i) Evaluation results using an experimental network of medical sensors (ii) Ensure data privacy and integrity (iii) Efficient communication between doctors and patients (iv) Malicious node detection (v) Considering heterogeneity of sensors | (i) Can have high storage overhead to store large number of keys |
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