Disengagement from social media platforms encompasses various behaviours, such as reducing activity or abandoning them altogether. At the far end of this spectrum, permanently deleting user accounts holds substantial implications for individuals, the people they contact, and the platform itself. However, the motivations underlying this choice, which involves erasing all content and social connections rather than simply abandoning the platform, remain unclear. To address this research gap, we analysed 4.5 million active users on Tumblr, a microblogging service. We assessed the likelihood of users deleting their accounts using binary logistic regression in general and in specific clusters of users. Our findings reveal that users are more inclined to delete their accounts if they have previously removed content from their profiles, highlighting the role of account deletion in cleaning and controlling personal information. Additionally, we observed robust peer correlations, indicating that users are more likely to delete their accounts if those they follow have already taken this step. We discuss these findings in the context of non-use theories and propose the development of checking-out processes that can enhance the user experience associated with account deletion.

Grid systems are proving increasingly useful for managing the batch computing jobs of organizations. One well-known example is Intel, whose internally developed NetBatch system manages tens of thousands of machines. The size, heterogeneity, and complexity of grid systems make them very difficult, however, to configure. This often results in misconfigured machines, which may adversely affect the entire system.We investigate a distributed data mining approach for detection of misconfigured machines. Our Grid Monitoring System (GMS) non-intrusively collects data from all sources (log files, system services, etc.) available throughout the grid system. It converts raw data to semantically meaningful data and stores this data on the machine it was obtained from, limiting incurred overhead and allowing scalability. Afterwards, when analysis is requested, a distributed outliers detection algorithm is employed to identify misconfigured machines. The algorithm itself is implemented as a recursive workflow of grid jobs. It is especially suited to grid systems, in which the machines might be unavailable most of the time and often fail altogether.

In this paper we explore an approach to privacy preserving data mining that relies on the k -anonymity model. The k -anonymity model guarantees that no private information in a table can be linked to a group of less than k individuals. We suggest extended definitions of k -anonymity that allow the k -anonymity of a data mining model to be determined. Using these definitions, we present decision tree induction algorithms that are guaranteed to maintain k -anonymity of the learning examples. Experiments show that embedding anonymization within the decision tree induction process provides better accuracy than anonymizing the data first and inducing the tree later.

This paper focuses on local computations of distributed aggregation problems on fixed graphs. We define a new metric on problem instances, Veracity Radius (VR), which captures the inherent possibility to compute them locally. We prove that VR yields a tight lower bound on output-stabilization time, i.e., the time until all nodes fix their outputs, as well as a lower bound on quiescence time. We present an efficient aggregation algorithm, I-LEAG, which reaches both output stabilization and quiescence within a time that is proportional to the VR of the problem instance, and is also efficient in terms of per-node communication and memory. We empirically show that the VR metric also effectively captures the performance of previously suggested efficient aggregation protocols, and that I-LEAG significantly outperforms these protocols in several respects.

Classification based on decision trees is one of the important problems in data mining and has applications in many fields. In recent years, database systems have become highly distributed, and distributed system paradigms such as federated and peer-to-peer databases are being adopted. In this paper, we consider the problem of inducing decision trees in a large distributed network of high dimensional databases. Our work is motivated by the existence of distributed databases in healthcare and in bioinformatics, and by the vision that these database are soon to contain large amounts of genomic data, characterized by its high dimensionality. Current decision tree algorithms would require high communication bandwidth when executed on such data, which is not likely to exist in large-scale distributed systems. We present an algorithm that sharply reduces the communication overhead by sending just a fraction of the statistical data. A fraction which is nevertheless sufficient to derive the exact same decision tree learned by a sequential learner on all the data in the network. Extensive experiments using standard synthetic SNP data show that the algorithm utilizes the high dependency among attributes, typical to genomic data, to reduce communication overhead by up to 99%. Scalability tests show that the algorithm scales well with both the size of the dataset, the dimensionality of the data, and the size of the distributed system.

Classification based on decision trees is one of the important problems in data mining and has applications in many fields. In recent years, database systems have become highly distributed, and distributed system paradigms, such as federated and peer-to-peer databases, are being adopted. In this paper, we consider the problem of inducing decision trees in a large distributed network of genomic databases. Our work is motivated by the existence of distributed databases in healthcare and in bioinformatics, and by the emergence of systems which automatically analyze these databases, and by the expectancy that these databases will soon contain large amounts of highly dimensional genomic data. Current decision tree algorithms require high communication bandwidth when executed on such data, which are large-scale distributed systems. We present an algorithm that sharply reduces the communication overhead by sending just a fraction of the statistical data. A fraction which is nevertheless sufficient to derive the exact same decision tree learned by a sequential learner on all the data-in the network. Extensive experiments using standard synthetic SNP data show that the algorithm utilizes the high dependency among attributes, typical to genomic data, to reduce communication overhead by up to 99 percent. Scalability tests show that the algorithm scales well with both the size of the data set, the dimensionality of the data, and the size of the distributed system.

Data privacy is a major concern that threatens the widespread deployment of data Grids in domains such as health-care and finance. We propose a unique approach for obtaining knowledge, by way of a data mining model, from a data Grid, while ensuring that the data is cryptographically safe. This is made possible by an innovative, yet natural generalization for the accepted trusted third party model and a new privacy-preserving data mining algorithm that is suitable for Grid-scale systems. The algorithm is asynchronous, involves no global communication patterns, and dynamically adjusts to changes in the data or to the failure and recovery of resources. To the best of our knowledge, this is the first privacy-preserving mining algorithm to possess these features. Simulations of thousands of resources prove that our algorithm quickly converges to the correct result while using reasonable communication. The simulations also prove that the effect of the privacy parameter on both the convergence time and the number of messages, is logarithmic.

Secure multiparty computation allows parties to jointly compute a function of their private inputs without revealing anything but the output. Theoretical results [2] provide a general construction of such protocols for any function. Protocols obtained in this way are, however, inefficient, and thus, practically speaking, useless when a large number of participants are involved.The contribution of this paper is to define a new privacy model -- k-privacy -- by means of an innovative, yet natural generalization of the accepted trusted third party model. This allows implementing cryptographically secure efficient primitives for real-world large-scale distributed systems.As an example for the usefulness of the proposed model, we employ k-privacy to introduce a technique for obtaining knowledge -- by way of an association-rule mining algorithm -- from large-scale Data Grids, while ensuring that the privacy is cryptographically secure.

We extend the problem of association rule mining--a key data mining problem--to systems in which the database is partitioned among a very large number of computers that are dispersed over a wide area. Such computing systems include grid computing platforms, federated database systems, and peer-to-peer computing environments. The scale of these systems poses several difficulties, such as the impracticality of global communications and global synchronization, dynamic topology changes of the network, on-the-fly data updates, the need to share resources with other applications, and the frequent failure and recovery of resources. We present an algorithm by which every node in the system can reach the exact solution, as if it were given the combined database. The algorithm is entirely asynchronous, imposes very little communication overhead, transparently tolerates network topology changes and node failures, and quickly adjusts to changes in the data as they occur. Simulation of up to 10,000 nodes show that the algorithm is local: all rules, except for those whose confidence is about equal to the confidence threshold, are discovered using information gathered from a very small vicinity, whose size is independent of the size of the system.

We present a new distributed association rule mining (D-ARM) algorithm that demonstrates superlinear speedup with the number of computing nodes. The algorithm is the first D-ARM algorithm to perform a single scan over the database. As such, its performance is unmatched by any previous algorithm. Scale-up experiments over standard synthetic benchmarks demonstrate stable run time regardless of the number of computers. Theoretical analysis reveals a tighter bound on error probability than the one shown in the corresponding sequential algorithm.

We extend the problem of association rule mining - a key data mining problem - to systems in which the database is partitioned among a very large number of computers that are dispersed over a wide area. Such computing systems include GRID computing platforms, federated database systems, and peer-to-peer computing environments. The scale of these systems poses several difficulties, such as the impracticality of global communications and global synchronization, dynamic topology changes of the network, on-the-fly data updates, the need to share resources with other applications, and the frequent failure and recovery of resources. We present an algorithm by which every node in the system can reach the exact solution, as if it were given the combined database. The algorithm is entirely asynchronous, imposes very little communication overhead, transparently tolerates network topology changes and node failures, and quickly adjusts to changes in the data as they occur. Simulation of up to 10000 nodes show that the algorithm is local: all rules, except for those whose confidence is about equal to the confidence threshold, are discovered using information gathered from a very small vicinity, whose size is independent of the size of the system.

Mining for associations between items in large transactional databases is a central problem in the field of knowledge discovery. When the database is partitioned among several share-nothing machines, the problem can be addressed using distributed data mining algorithms. One such algorithm, called CD, was proposed by Agrawal and Shafer in [1] and was later enhanced by the FDM algorithm of Cheung, Han et al. [5].