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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(More)
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.(More)
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(More)
Advancements in mobile computing techniques along with the pervasiveness of location-based services have generated a great amount of trajectory data. These data can be used for various data analysis purposes such as traffic flow analysis, infrastructure planning and understanding of human behavior. However, publishing this amount of trajectory data may lead(More)
Being able to detect faults in Hard Disk Drives (HDD) can lead to significant benefits to computer manufacturers, users and storage system providers. As a consequence, several works have focused on the development of fault detection algorithms for HDDs. Recently, promising results were achieved by methods using SMART (Self-Monitoring Analysis and Reporting(More)
Detecting faults in Hard Disk Drives (HDD) can lead to significant benefits to HDD manufacturers, users and storage system providers. As a consequence, several works have focused on the development of fault detection algorithms for HDDs. Recently, promising results were achieved by methods using SMART (Self-Monitoring Analysis and Reporting Technology)(More)
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