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%.

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.

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.

Differential privacy is a model which gives strong privacy guarantees. It was designed to make difficult to distinguish individuals' records on statistical databases while maximizing data utility. Differential privacy approaches usually assume that database records are sampled independently, i.e., each record of this database is independent of the rest. However, this assumption is not always true in the context of real-world applications. In this paper we propose DiPCoDing, a novel approach to calculate the correlation between records in statistical databases using clusterization. For this matter, we have considered Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Gaussian Mixture Model (GMM). Our method aims to group similar records, which are more likely to be correlated, to reduce the sensitivity of differential privacy and consequently the amount of noise added to the query answer, increasing data utility while providing privacy for correlated data. The experimental results of our approach showed that relative errors and noisy answers are significantly lower than those from existing works.

As the amount of collected social network information in RDF format grows, the development of solutions for the privacy of individuals, their attributes and relationships with others becomes an important subject of study. However, data privacy solutions are not well suitable for this specific type of data, mainly because they usually do not consider relationships between individuals, which are crucial to semantic data and social networks. Differential privacy is one of the most suitable techniques for statistical queries and, although it has been extensively studied in many papers, there is still much research to be done in this context. This paper presents two main contributions for privacy preserving statistic queries containing sensitive information about relationships between individuals. The first one is a complete approach to applying ϵ-differential privacy for RDF data and the second one presents an index-like data structure to efficiently compute parameters for the differential privacy mechanism: the query's actual value and data sensitivity for the given query. We conclude by evaluating our contributions over three real social network datasets presenting utility analysis for different values of ϵ. We also show the performance benefit of our index-like data structure for sensitivity calculation.

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.