598 research outputs found
Hybrid Cloud-Based Privacy Preserving Clustering as Service for Enterprise Big Data
Clustering as service is being offered by many cloud service providers. It helps enterprises to learn hidden patterns and learn knowledge from large, big data generated by enterprises. Though it brings lot of value to enterprises, it also exposes the data to various security and privacy threats. Privacy preserving clustering is being proposed a solution to address this problem. But the privacy preserving clustering as outsourced service model involves too much overhead on querying user, lacks adaptivity to incremental data and involves frequent interaction between service provider and the querying user. There is also a lack of personalization to clustering by the querying user. This work “Locality Sensitive Hashing for Transformed Dataset (LSHTD)” proposes a hybrid cloud-based clustering as service model for streaming data that address the problems in the existing model such as privacy preserving k-means clustering outsourcing under multiple keys (PPCOM) and secure nearest neighbor clustering (SNNC) models, The solution combines hybrid cloud, LSHTD clustering algorithm as outsourced service model. Through experiments, the proposed solution is able is found to reduce the computation cost by 23% and communication cost by 6% and able to provide better clustering accuracy with ARI greater than 4.59% compared to existing works
Security in Data Mining- A Comprehensive Survey
Data mining techniques, while allowing the individuals to extract hidden knowledge on one hand, introduce a number of privacy threats on the other hand. In this paper, we study some of these issues along with a detailed discussion on the applications of various data mining techniques for providing security. An efficient classification technique when used properly, would allow an user to differentiate between a phishing website and a normal website, to classify the users as normal users and criminals based on their activities on Social networks (Crime Profiling) and to prevent users from executing malicious codes by labelling them as malicious. The most important applications of Data mining is the detection of intrusions, where different Data mining techniques can be applied to effectively detect an intrusion and report in real time so that necessary actions are taken to thwart the attempts of the intruder. Privacy Preservation, Outlier Detection, Anomaly Detection and PhishingWebsite Classification are discussed in this paper
Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives
Consumer's privacy is a main concern in Smart Grids (SGs) due to the
sensitivity of energy data, particularly when used to train machine learning
models for different services. These data-driven models often require huge
amounts of data to achieve acceptable performance leading in most cases to
risks of privacy leakage. By pushing the training to the edge, Federated
Learning (FL) offers a good compromise between privacy preservation and the
predictive performance of these models. The current paper presents an overview
of FL applications in SGs while discussing their advantages and drawbacks,
mainly in load forecasting, electric vehicles, fault diagnoses, load
disaggregation and renewable energies. In addition, an analysis of main design
trends and possible taxonomies is provided considering data partitioning, the
communication topology, and security mechanisms. Towards the end, an overview
of main challenges facing this technology and potential future directions is
presented
Outlier-Resilient Web Service QoS Prediction
The proliferation of Web services makes it difficult for users to select the
most appropriate one among numerous functionally identical or similar service
candidates. Quality-of-Service (QoS) describes the non-functional
characteristics of Web services, and it has become the key differentiator for
service selection. However, users cannot invoke all Web services to obtain the
corresponding QoS values due to high time cost and huge resource overhead.
Thus, it is essential to predict unknown QoS values. Although various QoS
prediction methods have been proposed, few of them have taken outliers into
consideration, which may dramatically degrade the prediction performance. To
overcome this limitation, we propose an outlier-resilient QoS prediction method
in this paper. Our method utilizes Cauchy loss to measure the discrepancy
between the observed QoS values and the predicted ones. Owing to the robustness
of Cauchy loss, our method is resilient to outliers. We further extend our
method to provide time-aware QoS prediction results by taking the temporal
information into consideration. Finally, we conduct extensive experiments on
both static and dynamic datasets. The results demonstrate that our method is
able to achieve better performance than state-of-the-art baseline methods.Comment: 12 pages, to appear at the Web Conference (WWW) 202
Security in Data Mining-A Comprehensive Survey
Data mining techniques, while allowing the individuals to extract hidden knowledge on one
hand, introduce a number of privacy threats on the other hand. In this paper, we study some of these
issues along with a detailed discussion on the applications of various data mining techniques for
providing security. An efficient classification technique when used properly, would allow an user to
differentiate between a phishing website and a normal website, to classify the users as normal users
and criminals based on their activities on Social networks (Crime Profiling) and to prevent users from
executing malicious codes by labelling them as malicious. The most important applications of Data
mining is the detection of intrusions, where different Data mining techniques can be applied to
effectively detect an intrusion and report in real time so that necessary actions are taken to thwart the
attempts of the intruder
An Evolutionary Pentagon Support Vector Finder Method
In dealing with big data, we need effective algorithms; effectiveness that depends, among others, on the ability to remove outliers from the data set, especially when dealing with classification problems. To this aim, support vector finder algorithms have been created to save just the most important data in the data pool. Nevertheless, existing classification algorithms, such as Fuzzy C-Means (FCM), suffer from the drawback of setting the initial cluster centers imprecisely. In this paper, we avoid existing shortcomings and aim to find and remove unnecessary data in order to speed up the final classification task without losing vital samples and without harming final accuracy; in this sense, we present a unique approach for finding support vectors, named evolutionary Pentagon Support Vector (PSV) finder method. The originality of the current research lies in using geometrical computations and evolutionary algorithms to make a more effective system, which has the advantage of higher accuracy on some data sets. The proposed method is subsequently tested with seven benchmark data sets and the results are compared to those obtained from performing classification on the original data (classification before and after PSV) under the same conditions. The testing returned promising results
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