arXiv subjects

Mohsen Toorani

Publications and source records attributed to Mohsen Toorani.

6 recordsLinked to original sources

arXiv2026

Differentially Private EEG Feature Anonymization: A Privacy-Utility Case Study in Clinical Neurophysiology

Clinical electroencephalography (EEG) data are valuable for healthcare research and for developing artificial intelligence (AI)-based clinical decision-support systems, but EEG recordings and derived features may contain sensitive patient-specific information. This creates privacy risks when data are reused, analyzed, or shared across clinical and research environments. Conventional anonymization methods are often insufficient for high-dimensional biomedical signals, since removing direct identifiers does not necessarily prevent re-identification, linkage, or inference risks. At the same time, strong privacy protection may distort clinically relevant signal characteristics and reduce data utility. This paper studies subject-level differential privacy for protecting clinical EEG-derived feature representations using Gaussian and Laplace perturbations. The proposed framework considers three deployment scenarios: client-side anonymization, centralized server-side anonymization, and decentralized local training. Following EEG preprocessing and feature extraction, Gaussian and Laplace perturbations are applied to the resulting patient-level EEG feature representations. The Laplace experiments evaluate the implemented noise scales, while the scales required for formal full-vector calibration are derived separately. The effects of both perturbations are assessed using statistical utility measures and a downstream machine-learning-based utility check. The results show that differentially private perturbation can be integrated into EEG processing workflows, but the selected mechanism, privacy parameters, and sensitivity calibration strongly influence data utility. The study highlights the practical privacy-utility trade-off in DP-based EEG feature anonymization and the challenges of preserving downstream utility in small and imbalanced clinical EEG datasets.

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arXiv2026

Secure Aggregation for Privacy-Preserving Federated Learning on Clinical EEG Data

Federated learning enables multiple institutions to collaboratively train a shared model without exchanging their raw data. However, individual model updates are data-dependent and may reveal information about clients' local training data. This paper presents a privacy-preserving federated learning framework for clinical EEG data that uses masking-based secure aggregation as its core protection mechanism. The framework combines graph-based communication, threshold secret sharing, dropout recovery, local update clipping, an optional Bloom filter-based privacy-preserving record-linkage initialization module, and auxiliary-notary-based verifiability. It supports semi-honest and malicious aggregation settings and is implemented using the Flower federated learning framework. The secure aggregation variants are evaluated in a simulated cross-silo healthcare setting using TUH EEG-derived data under different client configurations. Under the stated assumptions, the secure variants hide individual updates from the aggregation server. The results show that these variants remain compatible with federated model training, although malicious-setting safeguards and lightweight consistency-checking mechanisms introduce additional computation, communication, and round-duration overhead. Among the proposed secure configurations, the base semi-honest variant incurs the lowest overhead; the malicious-server variants add protocol-consistency and authenticity safeguards, and the auxiliary-notary variants add lightweight aggregate-consistency checking.

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arXiv2023

Blockchain-based Decentralized Identity Management for Healthcare Systems

Blockchain-based decentralized identity management provides a promising solution to improve the security and privacy of healthcare systems and make them scalable. Traditional Identity Management Systems are centralized, which makes them single-point-of-failure, vulnerable to attacks and data breaches, and non-scalable. In contrast, decentralized identity management based on the blockchain can ensure secure and transparent access to patient data while preserving privacy. This approach enables patients to control their personal health data while granting permission for medical personnel to access specific information as needed. We propose a decentralized identity management system for healthcare systems named BDIMHS based on a permissioned blockchain with Hyperledger Indy and Hyperledger Aries. We develop further descriptions of required functionalities and provide high-level procedures for network initialization, enrollment, registration, issuance, verification and revocation functionalities. The proposed solution improves data security, privacy, immutability, interoperability, and patient autonomy by using selective disclosure, zero-knowledge proofs, Decentralized Identifiers, and Verifiable Credentials. Furthermore, we discuss the potential challenges associated with implementing this technology in healthcare and evaluate the performance and security of the proposed solution.

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arXiv2020

A Decentralized Dynamic PKI based on Blockchain

The central role of the certificate authority (CA) in traditional public key infrastructure (PKI) makes it fragile and prone to compromises and operational failures. Maintaining CAs and revocation lists is demanding especially in loosely-connected and large systems. Log-based PKIs have been proposed as a remedy but they do not solve the problem effectively. We provide a general model and a solution for decentralized and dynamic PKI based on a blockchain and web of trust model where the traditional CA and digital certificates are removed and instead, everything is registered on the blockchain. Registration, revocation, and update of public keys are based on a consensus mechanism between a certain number of entities that are already part of the system. Any node which is part of the system can be an auditor and initiate the revocation procedure once it finds out malicious activities. Revocation lists are no longer required as any node can efficiently verify the public keys through witnesses.

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arXiv2016

Security Protocols in a Nutshell

Security protocols are building blocks in secure communications. They deploy some security mechanisms to provide certain security services. Security protocols are considered abstract when analyzed, but they can have extra vulnerabilities when implemented. This manuscript provides a holistic study on security protocols. It reviews foundations of security protocols, taxonomy of attacks on security protocols and their implementations, and different methods and models for security analysis of protocols. Specifically, it clarifies differences between information-theoretic and computational security, and computational and symbolic models. Furthermore, a survey on computational security models for authenticated key exchange (AKE) and password-authenticated key exchange (PAKE) protocols, as the most important and well-studied type of security protocols, is provided.

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arXiv2015

On Vulnerabilities of the Security Association in the IEEE 802.15.6 Standard

Wireless Body Area Networks (WBAN) support a variety of real-time health monitoring and consumer electronics applications. The latest international standard for WBAN is the IEEE 802.15.6. The security association in this standard includes four elliptic curve-based key agreement protocols that are used for generating a master key. In this paper, we challenge the security of the IEEE 802.15.6 standard by showing vulnerabilities of those four protocols to several attacks. We perform a security analysis on the protocols, and show that they all have security problems, and are vulnerable to different attacks.

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