Table 1.
Comparative Analysis of Recent Techniques in Privacy-Preserving Data Aggregation for WBANs
| References | Key Algorithm or Methodology | Dataset Used | Research Gap Addressed | Result Outcomes |
|---|---|---|---|---|
| Reddy & Ram (2020) [11] | DAP-DS (Data Aggregation and Precedence by Delay Sensitivity) | Simulated WBAN topology using NS-3 | Absence of energy-efficient, delay-aware data aggregation in WBANs | More energy efficiency, less latency, and a higher packet delivery ratio. |
| Zhang & Dong (2023) [12] | Lightweight and secure aggregation scheme for anonymous multireceivers using ECC and pseudo-identities | Simulated data, no real-world dataset | WBANs lack anonymous, secure multi-receiver aggregation | Decreased transmission and communication costs and guaranteed data integrity and anonymity. |
| Almalki & Soufiene (2021)[13] | EPPDA (Efficient Privacy-Preserving Data Aggregation) using authentication tokens and ECC | Custom simulation (IoT-based healthcare data) | In IoT healthcare, privacy-preserving, verified data aggregation is required. | Verified by simulations, high security, low overhead, and effective aggregation. |
| Soufiene et al. (2020) [16] | PEERP (Priority-based Energy-Efficient Routing Protocol) | Simulated IoT-based healthcare scenario | Insufficient energy-efficient and QoS aware routing in healthcare IoT. | Reliable data transfer, minimal energy use, and high packet delivery. |
| Salehi Shahraki et al. (2023) [2] | Key management and access control protocols | Laboratory-based WBAN setups | Inefficiency in existing key management systems | Streamlined key management, improving trust and security. |
| Herbst et al. (2024) [3] | Token-based authentication and threat mitigation | Simulated medical data scenarios | Lack of device-specific security measures | Increased security through device authentication. |
| Pawar and Kalbande (2023) [4] | ECEBA protocol for optimizing QoS | Test bed with real sensor data | Poor quality of service in data transmission | Enhanced quality and efficiency of data transmission. |
| Nawaz et al. (2024) [17] | Methodology for patient prescreening in WBANs | Hospital trial data | Inefficient early detection mechanisms | Improved early detection leading to better patient outcomes. |
| Mehmood et al. (2023) [18] | QoS-based multi-path routing scheme | Simulated healthcare monitoring | Inadequate network reliability and performance | Increased reliability and enhanced network performance. |
| Memon et al. (2023) [6] | Probabilistic route stability protocol | Real-world WBAN implementations | Frequent route failures impacting data delivery | Stabilized routing, improving data delivery reliability. |
| Masood et al. (2024) [9] | Software-defined network management for energy efficiency | Energy consumption test scenarios | High energy consumption reducing operational longevity | Significantly reduced energy usage in WBANs. |
| Singh et al. (2024) [10] | Stable matching algorithms for revenue maximization in federated networks | Economic model simulations | Inefficient economic handling in network operations | Optimized revenue outcomes enhancing economic efficiency. |
| Verma & Gupta (2023) [19] | Pairing-free authentication and aggregation using bilinear mapping and hash functions | No standard dataset; simulation used | Pairing based techniques with high overhead in intelligent healthcare system. | Reduced processing costs, scalable, safe, and effective data aggregation. |
| Mehmood et, al. (2020) [14] | Trust-based ERCS (Trust-based Energy-Efficient and Reliable Communication | Real WBANtestbedwith8-12sensor nodes | Lack of trust management in energy-efficient routing; vulnerability to | 15¬20%energy savings, >97%PDR with20%maliciousnodes, enhanced security through behavioral trust scoring |
| Khan et al. (2024) [15] | Optimized Dynamic Attribute-Based Searchable Encryption (DABSE) using ECC on BN-256curve | Simulatedmedicaldatabase (10,000encrypted records) | Highcomputational overhead insearchableencryptionlimitingreal-timequeries; need for fine-grained-accesscontrol | 65%faster search (45-60mslatency), O(n log n) complexity,128 bitsecurity with 32 byte keys, maintains data utility postencryption |
Figure 1.
Demonstrate how the underlying technology and operating principles of privacy-preserving aggregation techniques can be used to categories them into major groupings
Table 2.
Datasets Usage Overview
| Dataset | Extracted Features | Sampling Rate Used | Payload Size in NS-3 |
|---|---|---|---|
| MIMIC-III | HR, BP, SpO2, respiration signals | 1–10 Hz | 32–64 bytes |
| PhysioNet ECG | ECG waveform samples | 125–250 Hz (down sampled to 50 Hz) | 64 bytes |
| UCI Arrhythmia | Attribute vectors | 1 Hz | 32 bytes |
| UCI Heart Disease | Feature vectors | 1 Hz | 32–40 bytes |
| Kaggle Activity | HR, activity, sleep indicators | 0.5–1 Hz | 40–64 bytes |
Table 3.
Implementation Parameters for Each Technique
| Technique | Library/Tool | Key Size/Parameter | Notes |
|---|---|---|---|
| QKD | Simulated BB84 protocol (QKDsim v2.1) | 128 bit | Quantum bit error rate = 2% |
| ABE | CP-ABE (Charm-Crypto 0.50) | 256-bit ECC keys | Attribute Count = 5 |
| Homomorphic Enc. | PySEAL 4.1 | 4096 polynomial modulus | CKKS Scheme |
| Differential Privacy | Numpy + Laplace noise | ε = 0.5–1.0 | - |
| Adaptive Streaming Filters | Custom NS-3 Module | - | Sampling interval = 1 s |
Table 4.
Datasets for Evaluating Privacy-Preserving Data Aggregation Techniques in WBANs. All Datasets were down sampled to 1Hz temporal resolution and normalized across channels prior to integration into the Ns-3 Traffic Generation model
| Dataset Name | Source | Data Contents | Size | Use Case | Accessibility |
|---|---|---|---|---|---|
| MIMIC-III | Beth Israel Deaconess MC | Patient vitals, lab tests, survival data, etc. | ~40,000 patients | Health monitoring in intensive care | Free (requires approval) |
| PhysioNet Challenge Datasets | PhysioNet | Various physiological signals | Varies | Testing algorithms on real signals | Free |
| Arrhythmia Dataset | UCI Machine Learning Repository | Attributes with heart arrhythmia features | 452 instances | Cardiac condition analysis | Free |
| Heart Disease Dataset | UCI Machine Learning Repository | Clinical heart disease records | 303 instances | Disease prediction and analysis | Free |
| Healthcare Activity Monitoring | Kaggle | Activity data, heart rate, sleep patterns | ~10,000 instances | Activity-based health tracking | Free |
| Simulated WBAN Data | Custom Simulation | Simulated vital signs and patient data | Configurable | Customizable scenarios for WBANs | Custom generated |
Table 5.
Privacy Preserving Scoring Rubric
| Score | Definition & Criteria |
|---|---|
| 1 – Very Low | No encryption; vulnerable to eavesdropping; no anonymity or key management. |
| 2 – Low | Basic symmetric encryption; susceptible to replay or compromise; no resistance to traffic analysis. |
| 3 – Moderate | Lightweight ECC or token-based authentication; moderate resistance to passive attackers. |
| 4 – High | Differential privacy (ε ≥ 1), CP-ABE, or authenticated encryption; resists common WBAN adversaries; low leakage probability. |
| 5 – Very High | Formal guarantees such as Homomorphic Encryption, QKD-based keying, or DP with ε < 1; resistant to strong adversaries; cryptographically provable security. |
Table 6.
Data Integrity Scoring Rubric
| Score | Definition & Criteria |
|---|---|
| 1 | No checksum or integrity check. |
| 2 | Basic checksum; vulnerable to tampering. |
| 3 | MAC or hash-based verification (SHA-1/SHA-256). |
| 4 | ECC/ABE-based authenticated integrity; tamper detection with low false-positive rate. |
| 5 | Fully verifiable aggregation (HE or signature-based); strong resistance to forgery. |
Figure 2.
Visual comparison of key performance trends (Privacy, latency, throughput, and energy consumption) of WBAN data aggregation technique’s based on representation values reported in prior studies
Table 7.
Comparative summary of reported performance characteristics of selected data Aggregation Techniques for WBANs on tends and representative values from prior studies
| Technique | Privacy Preservation (Scale 1¬5) | Data Integrity (Scale 1¬5) | Throughput (data/min) | Latency (ms) | Energy Consumption (J) | Accuracy of Data Aggregation (%) |
|---|---|---|---|---|---|---|
| Edge Computing Based Aggregation | 4.0 | 4.0 | 340±12.5 | 85±5.1 | 0.38±0.03 | 92.0±1.8 |
| Quantum Key Distribution (QKD) | 5.0 | 5.0 | 280±15.3 | 130±8.2 | 0.60±0.04 | 95.0±1.2 |
| Lightweight Homomorphic Encryption | 5.0 | 4.0 | 250±18.7 | 150±9.7 | 0.65±0.05 | 90.0±2.1 |
| AI-Enhanced Anonymization | 4.0 | 3.0 | 320±14.2 | 95±6.3 | 0.42±0.03 | 88.0±2.5 |
| Differential Privacy Techniques | 5.0 | 4.0 | 300±13.8 | 105±7.1 | 0.45±0.04 | 91.0±1.9 |
| Adaptive Streaming Filters | 3.0 | 3.0 | 350±11.3 | 80±4.3 | 0.33±0.02 | 85.0±2.8 |
Table 8.
Indicative Network-level performance characteristics of Data Aggregation Techniques in WBANs as reported in existing literature
| Technique | Packet Delivery Ratio (%) | Error Rate (%) | Network Latency (ms) | System Efficiency (Scale 1-5) |
|---|---|---|---|---|
| Edge Computing-Based Aggregation | 98.5±0.6 | 0.5±0.12 | 85±5.1 | 4.0 |
| Quantum Key Distribution (QKD) | 96.0±1.2 | 0.2±0.08 | 130±8.2 | 3.0 |
| Lightweight Homomorphic Encryption | 94.5±1.5 | 0.8±0.15 | 150±9.7 | 3.0 |
| AI-Enhanced Anonymization | 97.0±0.9 | 0.6±0.11 | 95±6.3 | 4.0 |
| Differential Privacy Techniques | 95.0±1.1 | 0.7±0.13 | 105±7.1 | 4.0 |
| Adaptive Streaming Filters | 99.0±0.8 | 0.4±0.09 | 80±4.3 | 5.0 |
Figure 3.
Illustrative comparison of network performance trends among WBAN data aggregation techniques derived from reported packet delivery, latency, and efficiency metrics in existing research