Hard Disk Drive (HDD) failures are a persistent and costly problem in data centers, making the accurate prediction of Remaining Useful Life (RUL) essential for mitigating downtime and data loss. While machine learning models, particularly LSTMs, have been widely applied to this task using Self-Monitoring, Analysis, and Reporting Technology (SMART) data, their effectiveness is largely confined to short-term predictions (e.g., 30 days), lacking the ability to accurately forecast RUL over longer horizons, such as one year. To address these limitations, this paper proposes a novel methodology. First, we create a Binary Classification Model that discriminates input data as healthy or faulty. Then, we use its output to generate failure likelihood features to append to the original dataset. We evaluate our approach on a real-world dataset from Backblaze, comparing its performance against baseline models to demonstrate that incorporating the auxiliary features improves the MSE score of the RUL predictions up to 38%.

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.

Many complex natural and technological systems are commonly modeled as count-weighted graphs, where nodes represent entities, edges model relationships between them, and edge weights define some counting statistics associated with each relationship. As graph data usually contain sensitive information about entities, preserving privacy when releasing this type of data becomes an important issue. In this context, differential privacy (DP) has become the de facto standard for data release under strong privacy guarantees. When dealing with DP for weighted graphs, most state-of-the-art works assume that the graph topology is known. However, in several real-world applications, the privacy of the graph topology also needs to be ensured. In this paper, we aim to bridge the gap between DP and count-weighted graph data release, considering both graph structure and edge weights as private information. We first adapt the weighted graph DP definition to take into account the privacy of the graph structure. We then develop two novel approaches to privately releasing count-weighted graphs under the notions of global and local DP. We also leverage the post-processing property of DP to improve the accuracy of the proposed techniques considering graph domain constraints. Experiments using real-world graph data demonstrate the superiority of our approaches in terms of utility over existing techniques, enabling subsequent computation of a variety of statistics on the released graph with high utility, in some cases comparable to the non-private results.

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.

Differential privacy is the state-of-the-art formal definition for data release under strong privacy guarantees. A variety of mechanisms have been proposed in the literature for releasing the noisy output of numeric queries (e.g., using the Laplace mechanism), based on the notions of global sensitivity and local sensitivity. However, although there has been some work on generic mechanisms for releasing the output of non-numeric queries using global sensitivity (e.g., the Exponential mechanism), the literature lacks generic mechanisms for releasing the output of non-numeric queries using local sensitivity to reduce the noise in the query output. In this work, we remedy this shortcoming and present the local dampening mechanism. We adapt the notion of local sensitivity for the non-numeric setting and leverage it to design a generic non-numeric mechanism. We illustrate the effectiveness of the local dampening mechanism by applying it to two diverse problems: (i) Influential node analysis. Given an influence metric, we release the top-k most influential nodes while preserving the privacy of the relationship between nodes in the network; (ii) Decision tree induction. We provide a private adaptation to the ID3 algorithm to build decision trees from a given tabular dataset. Experimental results show that we could reduce the use of privacy budget by 3 to 4 orders of magnitude for Influential node analysis and increase accuracy up to 12% for Decision tree induction when compared to global sensitivity based approaches.

Cloud computing is a successful, emerging paradigm that supports on-demand services with pay-as-you-go model. With the exponential growth of data, NoSQL databases have been used to manage data in the cloud. In these newly emerging settings, mechanisms to guarantee Quality of Service heavily relies on performance predictability, i.e., the ability to estimate the impact of concurrent query execution on the performance of individual queries in a continuously evolving workload. This paper presents a performance modeling approach for NoSQL databases in terms of performance metrics which is capable of capturing the non-linear effects caused by concurrency and distribution aspects. Experimental results confirm that our performance modeling can accurately predict mean response time measurements under a wide range of workload configurations.