arXiv subjects

Morteza Amini

Publications and source records attributed to Morteza Amini.

17 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.

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arXiv2026

Variational Bayesian Sparse Negative Binomial Regression

Count data with overdispersion and high-dimensional predictors pose significant challenges in modern applications. While negative binomial regression offers a flexible modeling framework, existing Bayesian approaches rely on computationally expensive MCMC methods that become impractical in high-dimensional settings. This paper develops a variational Bayesian framework for sparse negative binomial regression using horseshoe and continuous spike-and-slab priors. Our proposed methods achieve estimation accuracy and variable selection performance comparable to MCMC benchmarks while offering substantial computational savings over MCMC. Extensive simulations demonstrate that the negative binomial specification is essential for overdispersed data, as Poisson-based approaches exhibit substantial performance degradation under overdispersion. Conversely, our methods remain robust when the data are Poisson, making them a safer default choice. Applications to real benchmark datasets further confirm the practical utility of our approach.

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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.

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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.

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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.

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arXiv2025

On weight and variance uncertainty in neural networks for regression tasks

We investigate the problem of weight uncertainty originally proposed by [Blundell et al. (2015). Weight uncertainty in neural networks. In International conference on machine learning, 1613-1622, PMLR.] in the context of neural networks designed for regression tasks, and we extend their framework by incorporating variance uncertainty into the model. Our analysis demonstrates that explicitly modeling uncertainty in the variance parameter can significantly enhance the predictive performance of Bayesian neural networks. By considering a full posterior distribution over the variance, the model achieves improved generalization compared to approaches that treat variance as fixed or deterministic. We evaluate the generalization capability of our proposed approach through a function approximation example and further validate it on the riboflavin genetic dataset. Our exploration encompasses both fully connected dense networks and dropout neural networks, employing Gaussian and spike-and-slab priors respectively for the network weights, providing a comprehensive assessment of how variance uncertainty affects model performance across different architectural choices.

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arXiv2023

Variational Inference for Sparse Poisson Regression

We have utilized the non-conjugate Variational Bayesian (VB) method for the problem of the sparse Poisson regression model. To provide approximate conjugacy in the model, the likelihood is approximated by a quadratic function, yielding conjugacy between the approximation component and the Gaussian prior on the regression coefficient. Three sparsity-enforcing priors (Laplace, Continuous Spike and Slab, and Bernoulli) are used for this problem. The proposed models are compared with each other, the associated MCMC models, and two frequentist sparse Poisson methods (LASSO and SCAD) to evaluate their estimation, prediction, and sparsity performance. In a simulation study, the proposed VB methods closely approximate the posterior parameter distribution while achieving significantly faster computation than benchmark MCMC methods. Using several benchmark count response data sets, the prediction performance of the proposed methods is evaluated in real-world applications.

stat.ME↗

arXiv2022

Functional Regression Models with Functional Response: A New Approach and a Comparative Study

This paper proposes a new nonlinear approach for additive functional regression with functional response based on kernel methods along with some slight reformulation and implementation of the linear regression and the spectral additive model. The latter methods have in common that the covariates and the response are represented in a basis and so, can only be applied when the response and the covariates belong to a Hilbert space, while the proposed method only uses the distances among data and thus can be applied to those situations where any of the covariates or the response is not Hilbert, typically normed or even metric spaces with a real vector structure. A comparison of these methods with other procedures readily available in R is preformed in a simulation study and in real datasets showing the results of the advantages of the nonlinear proposals and the small loss of efficiency when the simulation scenario is truly linear. The comparison is done in the Hilbert case as it is the only scenario where all the procedures can be compared. Finally, the supplementary material provides a visualization tool for checking the linearity of the relationship between a single covariate and the response, another real data example, and a link to a GitHub repository where the code and data are available.} %and an example considering that the response is not Hilbertian.

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arXiv2021

Genomic Data Analysis using a Two Stage Expectation Propagation Algorithm for Analysis of Sparse Bayesian High-Dimensional Instrumental Variables Regression

Simultaneous analysis of gene expression data and genetic variants is highly of interest, especially when the number of gene expressions and genetic variants are both greater than the sample size. Association of both causal genes and effective SNPs makes the use of sparse modeling of such genetic data sets, highly important. The high-dimensional sparse instrumental variables models are one of such useful association models, which models the simultaneous relation of the gene expressions and genetic variants with complex traits. From a Bayesian viewpoint, the sparsity can be favored using sparsity-enforcing priors such as spike-and-slab priors. A two-stage modification of the expectation propagation (EP) algorithm is proposed and examined for approximate inference in high-dimensional sparse instrumental variables models with spike-and-slab priors. This method is an adoption of the classical two-stage least squares method, to be used with the Bayes context. A simulation study is performed to examine the performance of the methods. The proposed method is applied to analysis of the mouse obesity data.

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arXiv2021

Hhsmm: An R package for hidden hybrid Markov/semi-Markov models

This paper introduces the hhsmm R package, which involves functions for initializing, fitting, and predication of hidden hybrid Markov/semi-Markov models. These models are flexible models with both Markovian and semi-Markovian states, which are applied to situations where the model involves absorbing or macro-states. The left-to-right models and the models with series/parallel networks of states are two models with Markovian and semi-Markovian states. The hhsmm also includes Markov/semi-Markov switching regression model as well as the auto-regressive HHSMM, the nonparametric estimation of the emission distribution using penalized B-splines, prediction of future states and the residual useful lifetime estimation in the predict function. The commercial modular aero-propulsion system simulation (C-MAPSS) data-set is also included in the package, which is used for illustration of the application of the package features. The application of the hhsmm package to the analysis and prediction of the Spain's energy demand is also presented.

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arXiv2017

Android Malware Detection using Markov Chain Model of Application Behaviors in Requesting System Services

Widespread growth in Android malwares stimulates security researchers to propose different methods for analyzing and detecting malicious behaviors in applications. Nevertheless, current solutions are ill-suited to extract the fine-grained behavior of Android applications accurately and efficiently. In this paper, we propose ServiceMonitor, a lightweight host-based detection system that dynamically detects malicious applications directly on mobile devices. ServiceMonitor reconstructs the fine-grained behavior of applications based on a novel systematic system service use analysis technique. Using proposed system service use perspective enables us to build a statistical Markov chain model to represent what and how system services are used to access system resources. Afterwards, we consider built Markov chain in the form of a feature vector and use it to classify the application behavior into either malicious or benign using Random Forests classification algorithm. ServiceMonitor outperforms current host-based solutions with evaluating it against 4034 malwares and 10024 benign applications and obtaining 96\% of accuracy rate and negligible overhead and performance penalty.

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arXiv2015

Nonparametric estimation of the entropy using a ranked set sample

This paper is concerned with non-parametric estimation of the entropy in ranked set sampling. Theoretical properties of the proposed estimator are studied. The proposed estimator is compared with the rival estimator in simple random sampling. The applications of the proposed estimator to the mutual information estimation as well as estimation of the Kullback-Leibler divergence are provided. Several Monte-Carlo simulation studies are conducted to examine the performance of the estimator. The results are applied to the long-leaf pine (pinus palustris) trees and the body fat percentage data sets to illustrate applicability of theoretical results.

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arXiv2015

Estimation of the parameter of a dynamically selected population for two subclasses of the exponential family

We introduce the problem of estimation of the parameters of a dynamically selected population in an infinite sequence of random variables and provide its application in the statistical inference based on record values from a non-stationary scheme. We develop unbiased estimation of the parameters of the dynamically selected population and evaluate the risk of the estimators. We provide comparisons with natural estimators and obtain asymptotic results. Finally, we illustrate the applicability of the results using real data.

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arXiv2015

Bayesian prediction of minimal repair times of a series system based on hybrid censored sample of components' lifetimes under Rayleigh distribution

In this paper, we develop Bayesian predictive inferential procedures for prediction of repair times of a series system, applying a minimal repair strategy, using the information contained in an independent observed hybrid censored sample of the lifetimes of the components of the system, assuming the underlying distribution of the lifetimes to be Rayleigh distribution. An illustrative real data example and a simulation study are presented for the purpose of illustration and comparison of the proposed predictors.

math.ST↗

arXiv2014

Optimal design for step-stress accelerated test with random discrete stress elevating times based on gamma degradation process

Recently, a step-stress accelerated degradation test (SSADT) plan, in which the stress level is elevated when the degradation value of a product crosses a pre-specified value, was proposed. The times of stress level elevating are random and vary from product to product. In this paper we extend this model to a more economic plan. The proposed extended model has two economical advantages compared with the previous one. The first is that the times of stress level elevating in the new model are identical for all products, which enable us to use only one chamber (oven) for testing all test units. The second is that, the new method does not require continuous inspection and to elevate the stress level, it is not necessary for the experimenter to inspect the value of the degradation continually. The new method decrease the cost of measurement and also there is no need to use electronic sensors to detect the first passage time of the degradation to the threshold value in the new method. We assume that the degradation path follows a gamma process. The stress level is elevated as soon as the measurement of the degradation of one of the test units, at one of the specified times, exceeds the threshold value. Under the constraint that the total experimental cost does not exceed a pre-specified budget, the optimal settings including the optimal threshold value, sample size, measurement frequency and termination time are obtained by minimizing the asymptotic variance of an estimated quantile of the lifetime distribution of the product. A case study is presented to illustrate the proposed method.

stat.AP↗

arXiv2014

Failure Inference and Optimization for Step Stress Model Based on Bivariate Wiener Model

In this paper, we consider the situation under a life test, in which the failure time of the test units are not related deterministically to an observable stochastic time varying covariate. In such a case, the joint distribution of failure time and a marker value would be useful for modeling the step stress life test. The problem of accelerating such an experiment is considered as the main aim of this paper. We present a step stress accelerated model based on a bivariate Wiener process with one component as the latent (unobservable) degradation process, which determines the failure times and the other as a marker process, the degradation values of which are recorded at times of failure. Parametric inference based on the proposed model is discussed and the optimization procedure for obtaining the optimal time for changing the stress level is presented. The optimization criterion is to minimize the approximate variance of the maximum likelihood estimator of a percentile of the products' lifetime distribution.

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arXiv2011

Improved estimator of the entropy and goodness of fit tests in ranked set sampling

The entropy is one of the most applicable uncertainty measures in many statistical and en- gineering problems. In statistical literature, the entropy is used in calculation of the Kullback- Leibler (KL) information which is a powerful mean for performing goodness of fit tests. Ranked Set Sampling (RSS) seems to provide improved estimators of many parameters of the popu- lation in the huge studied problems in the literature. It is developed for situations where the variable of interest is difficult or expensive to measure, but where ranking in small sub-samples is easy. In This paper, we introduced two estimators for the entropy and compare them with each other and the estimator of the entropy in Simple Random Sampling (SRS) in the sense of bias and Root of Mean Square Errors (RMSE). It is observed that the RSS scheme would improve this estimator. The best estimator of the entropy is used along with the estimator of the mean and two biased and unbiased estimators of variance based on RSS scheme, to esti- mate the KL information and perform goodness of fit tests for exponentiality and normality. The desired critical values and powers are calculated. It is also observed that RSS estimators would increase powers.

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