Differentially private selection mechanisms offer strong privacy guarantees for queries aiming to identify the top-scoring element$r$from a finite set$\mathfrak{R}$, based on a dataset-dependent utility function. While selection queries are fundamental in data science, few mechanisms effectively ensure their privacy. Furthermore, most approaches rely on global sensitivity to achieve differential privacy (DP), which can introduce excessive noise and impair downstream inferences. To address this limitation, we propose the Smooth Noisy Max (SNM) mechanism, which leverages smooth sensitivity to yield provably tighter (upper bounds on) expected errors compared to global sensitivity-based methods. Empirical results demonstrate that SNM is more accurate than state-of-the-art differentially private selection methods in three applications: percentile selection, greedy decision trees, and random forests.

Identifying Customer Induced Damage (CID) is a key part in warranty programs of electronics manufacturers. CID is defined as any damage in the unit performed by an unauthorized person including the customer. In such cases, damaged units are not covered by warranty. An important aspect of CID inspection activities is that, in most industries, the task is performed manually. Since such task demands high attention to details, human mistakes occur very often. For such reasons, CID detection can be considered as a good candidate to be handled by automatic detection methods with computer vision tools. In this work, we evaluate modern computer vision methods based on deep neural networks (SwinSoft Teacher and Mask-RCNN as prediction networks with swin and resnet backbones) for automatic CID detection in motherboard images. All images were originated from private company repair centers around the globe. We highlight the following results for Average Precision (AP) and Average Recall (AR):$AP{@}0.5 =\mathbf{80.2}$and$AR^{m}=\mathbf{57.9}$, for Mask-RCNN with Swin-S backbone, where the variant$m$means medium, and$AP{@}0.5 =\mathbf{74.2}$and$AR^{m}=\mathbf{45.4}$, for Mask-RCNN Swin-T backbone. Our results demonstrate that Swin-based methods had the best overall results and exhibit interesting characteristics for our application.

Predicting failures in Hard Disk Drives (HDD) is a major challenge that has been faced by both industry and academy in recent years. Being able to predict failure events may incur in avoiding data losses and also improve service availability. Among all failure prediction strategies, the health degree prediction is one of the most popular. The task of health degree prediction consists of, given a finite set of health states that are related to the degradation of the equipment, estimate which state reflects the actual degradation of the equipment. This problem is usually modeled as a classification task. Although many health degree prediction methods have been proposed, some practical details regarding this prediction task have been neglected in previous works. In this work we tackle two of these aspects: the ordinal nature of the problem and the different costs associated with miss-classifications. The problem can be considered as ordinal since classifying a HDD in a health level that is far from is true health level shall be more penalized than classifying in a near health level, thus a classical classification framework is not recommended. The different costs associated with mis-classifications are related to the fact that early predictions are preferred than late prediction since the later can result in failures. Such aspects are considered in a framework based on Deep Recurrent Neural Networks (DRNN). The choice of DRNN is given its remarkable performances in many applications including HDDs failure prediction. The resulting methods outperformed state-of-the-art approaches in a metric that consider the new aspects that motivated our proposal.

Nowadays, predicting failures in Hard Disk Drives (HDD) is of key importance for storage service providers and end users. Being able to detect in advance that a disk is going to fail may enable maintenance actions that can avoid severe data losses. For that reason, many researchers had devoted attention to this research topic. Recently, several authors have reported promising results by using attributes collected by the SMART (Self-Monitoring, Analysis and Reporting Technology) system along with machine learning methods. Although the best results were obtained by supervised machine learning methods, it is important to notice that data from degraded HDDs is scarce. Hence, anomaly detection methods arise a promising solution. Among such methods, recent works reported that reconstruction based anomaly detection algorithms had the best performance on HDDs fault detection. In line with such results, in this paper we aim to further investigate the performance of such methods. We conducted tests with classical PCA based methods and neural autoencoder based methods. In addition to testing with the popular reconstruction based autoencoder method we also evaluated a method that analyzes the distribution of the latent space. Additionally we propose a simple formulation to combine both methods. On the basis of our experiments, we verified that autoencoder based methods had the best performances according to the two evaluation metrics. Among such methods, the combination approach had the best overall performance.

The ability to predict failures in Hard Disk Drives (HDD) is a major objective of HDD manufacturers since avoiding unexpected failures may prevent data loss. As a consequence, failure prediction in HDDs became a topic that attracted much attention in recent years. Nowadays, most HDDs are equipped with a threshold-based monitoring system named Self-Monitoring, Analysis and Reporting Technology (SMART). The system collects several performance parameters and detects anomalies that may indicate incipient failures. Although the SMART system is very popular, it achieves failure detection rates of 3% to 10%. Moreover, SMART works as an incipient failure detection method and does not provide an estimate of the remaining life of the HDD. In this paper, we propose a failure prediction method using SMART attributes and a Bayesian Network. The proposed method, named Bayesian network based Method for Failure prediction in HDDs (BNFH) uses a subset of the SMART attributes and a set of SMART trend related attributes to provide remaining life estimates of HDDs. To demonstrate practical usefulness, this method was applied to a dataset consisting of 49,056 hard drives from Backblaze's data centers.

Hard Disk Drives (HDD) failure prediction is a challenging topic that has attracted much attention in recent years. Predicting failures in HDD may avoid losing data thus improving data reliability. Previous works on failure prediction are based on parametric approaches that model healthy drives with a Gaussian distribution. Although they achieved good results, the Gaussianity assumption may not hold true. The following work proposes a method for fault detection in HDD based on a Gaussian Mixture Model. A self-monitoring, analysis, and reporting technology dataset is used to evaluate the proposed method. Results show that the method outperforms previous works in both fault detection and time before failure.