arXiv subjects

Farid Zaredar

Publications and source records attributed to Farid Zaredar.

4 recordsLinked to original sources

arXiv2026

SP2UBI: Secure and Privacy-Preserving Usage-Based Insurance

The transition from traditional auto insurance, whose basis is defined by a set of static parameters, like the age of driver and number of accidents, to Usage-Based Insurance, whose pricing is based on driving behavior, was boosted by Intelligent Transportation Systems. At the same time, the analysis of high-resolution telematics data might reveal users' behavior and habits, which is associated with significant privacy concerns. The majority of privacy-preserving UBI systems are subject to policyholders' data leaks at some point during the life cycle of their protocol. Moreover, it is challenging to ensure data integrity against possible intentional or unintentional sensor anomalies (like sensor spoofing or malfunctioning) since the approaches used in this case require privacy-compromising audits and do not consider the oracle problem. In order to address the issues, we propose SP2UBI, a privacy-preserving UBI solution which guarantees mutual confidentiality. SP2UBI collects the telematics data in coarse-grained, statistical form without any spatiotemporal identifiers so that it is impossible to reconstruct fine-grained mobility traces. By utilizing the Torus Fully Homomorphic Encryption scheme, which incurs low computational overhead, computations are performed directly over encrypted data. This way, the insurer calculates risk factors without accessing any sensitive information, while parameters of its risk model are confidential. In order to protect data integrity against sensor level manipulation, SP2UBI incorporates a speed verification system assisted by Integrated Sensing and Communications (ISAC) technology which is capable of detecting fraud while preserving user privacy. Experimental evaluation shows that one round of protocol execution takes 41.2 ms, showing that our framework is lightweight and preserves more privacy guarantees compared to state-of-the-art solutions.

cs.CR↗

arXiv2025

A Lightweight Incentive-Based Privacy-Preserving Smart Metering Protocol for Value-Added Services

The emergence of smart grid and advanced metering infrastructure (AMI) has revolutionized energy management. AMI has enabled a wide variety of demand- and supply-side management utilities, including billing, outage detection, grid monitoring, load forecasting, and value-added services. In AMI, meters deliver consumption values at predefined intervals to the utility provider (UP), and such readings can raise privacy violation issues. With such data, an adversary can deduce information about individuals' life patterns and the types of electrical and smart devices within their homes. In this paper, we propose a lightweight, privacy-preserving smart metering protocol to provide incentive-based value-added services. The scheme employs temporal-based aggregation and local differential privacy to enable reporting of coarse-grained consumption values with adjustable granularity while limiting the information revealed to the UP. Hash-chain credentials and blind digital signatures enable anonymous authentication of reported readings, while pseudonyms and anonymous overlay networks conceal the source of reports, providing anonymity and unlinkability between customers and their reported consumption values. This approach protects consumers' privacy while preserving data utility and enables automatic token redemption at the specified time. Finally, we evaluate our protocol from two aspects: performance and privacy. With a 2048-bit RSA key size, a 7-day program duration, and four reports per day, our complete protocol implementation takes about 3.36 seconds and consumes an average of 4.5 MB of memory. Our formal analysis demonstrates consumption-value privacy, participant anonymity, and issuance--reporting unlinkability against semi-trusted aggregators and utility providers, while resisting untrusted active and passive adversaries.

cs.CR↗

arXiv2025

A Collusion-Resistance Privacy-Preserving Smart Metering Protocol for Operational Utility

Modern smart grids rely on advanced metering infrastructure (AMI) to collect fine-grained consumption readings for operational services such as grid monitoring, load forecasting, and demand--supply balancing. However, these high-frequency readings can reveal sensitive information about consumers' daily activities. To address this privacy concern, we propose a collusion-resistant privacy-preserving aggregation protocol for smart metering operational services. The protocol distributes noise-cancellation responsibility among a configurable group of $K$ designated smart meters. Each non-designated meter perturbs its reading using $K$ independent noise components, while corresponding cancellation values ensure that noise is removed only from the final aggregate. The protocol combines Paillier homomorphic encryption with a KEM--KDF--AEAD construction. Paillier encryption enables the aggregator to compute an encrypted aggregate without decrypting individual contributions, while authenticated encryption protects exchanged noise components between smart meters. Under the considered collusion and meter-exposure model, the exact reading of a trusted and unexposed meter remains protected as long as at least one designated and one non-designated meter remain unexposed. We evaluate the protocol in terms of computational, memory, communication, and privacy overheads. Privacy is evaluated using normalized conditional entropy (NCE) and normalized root-mean-square error (NRMSE). The results show that increasing the noise scale increases NCE and uncertainty about individual readings, while NRMSE quantifies the gradual loss of privacy as additional opposite-role meters are exposed. Overall, the protocol provides exact aggregate consumption values required for operational services while protecting individual fine-grained readings against the considered adversarial coalition.

cs.CR↗

arXiv2025

A Lightweight Privacy-Preserving Smart Metering Billing Protocol with Dynamic Tariff Policy Adjustment

Fine-grained smart-meter consumption data are essential for real-time-tariff billing, but their collection can reveal sensitive information about customers' consumption patterns. To address this privacy challenge, we propose a lightweight privacy-preserving smart metering protocol for real-time tariff billing with dynamic tariff policy adjustment. Our scheme employs a data perturbation mechanism with a tariff-weighted zero-sum property, allowing the utility provider to compute the exact customer bill from perturbed consumption readings. The protocol further supports tariff policy adjustments after the consumption readings have been reported. For proportional (Type I) adjustments, previously reported noisy readings are reused without any additional smart-meter report. For non-proportional (Type II) adjustments, the first $L-1$ noisy readings (where L is the number of intervals) are reused and only the final corrected noisy consumption value is updated, reducing additional communication, computation, and storage resources. The number of accepted Type II adjustments is bounded to limit additional exact algebraic information introduced by repeated non-proportional tariff changes. We evaluate the scheme in terms of computational, memory, communication, and privacy characteristics. The protocol requires approximately $3.94540$ seconds of execution time for a complete year, and results confirm exact billing to numerical precision. Privacy is evaluated through statistical characterization, reconstruction-based analysis, tariff-adjustment analysis, and Jensen--Shannon divergence. Increasing the evaluated noise scale reduces the target-specific reconstruction value available to the considered attack while increasing the distributional difference between the original and perturbed consumption data.

cs.CR↗