When considering a secure IoT-integrated WBAN (Wireless Body Area Network) framework for e-healthcare, there are several schemes that could potentially perform better than the improved Feige-Fiat-Shamir (IFFS) scheme. Here are a few alternatives worth considering:

Attribute-Based Encryption (ABE): ABE is a cryptographic scheme that allows access control based on attributes. It provides fine-grained access control, which can be beneficial in e-healthcare scenarios where different parties require access to specific data based on attributes like role, credentials, or patient conditions. ABE can be utilized to ensure secure data sharing within the WBAN framework.

Fully Homomorphic Encryption (FHE): FHE enables computations on encrypted data without decryption, allowing computations to be performed directly on encrypted data. In the context of an IoT-integrated WBAN framework, FHE can be employed to preserve the privacy of sensitive health data while allowing computations on the encrypted data. This can enhance privacy and security in e-healthcare applications.

Secure Multi-Party Computation (MPC): MPC allows multiple parties to jointly compute a function while keeping their individual inputs private. In the context of e-healthcare, MPC can enable secure computations on sensitive health data collected from different WBAN devices without exposing the raw data. It provides a privacy-preserving approach for collaborative data analysis and decision-making.

Zero-Knowledge Proofs (ZKP): ZKP protocols allow one party, the prover, to prove a statement to another party, the verifier, without revealing any additional information beyond the validity of the statement. ZKP can be applied in e-healthcare scenarios to provide proof of data integrity or authenticity without disclosing the actual data. This ensures privacy while enabling trust in the data collected from IoT devices.

Secure Multiparty Machine Learning: Secure multiparty machine learning techniques, such as federated learning or secure aggregation, can be employed in the WBAN framework to train machine learning models on the distributed data from multiple devices without sharing the raw data. These techniques enable collaborative model training while preserving the privacy of individual data sources.

It is worth noting that the choice of the scheme depends on the specific requirements, constraints, and threat models of the IoT-integrated WBAN framework for e-healthcare. Evaluating the performance, security guarantees, and efficiency of these schemes in the context of the intended application is crucial for selecting an appropriate solution. Additionally, considering a combination of multiple schemes may provide enhanced security and privacy for the framework.

Dear Opeyemi A. Ajibola,

You may want to look over the following data:

An improved parallel interactive Feige-Fiat-Shamir identification scheme with almost zero soundness error and complete zero-knowledge

https://ieeexplore.ieee.org/document/6906641

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Fiat-Shamir and Correlation Intractability from Strong KDM-Secure Encryption

https://eprint.iacr.org/2018/131.pdf

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Feige-Fiat-Shamir and Zero Knowledge Proof

https://medium.com/asecuritysite-when-bob-met-alice/feige-fiat-shamir-and-zero-knowledge-proof-cdd2a972237c

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Thank you Haroon Rashid Hammood Al-Dallal.

I appreciate you Len Leonid Mizrah