Privacy preserving association rule mining using attribute-identity mapping

Association rule mining uncovers hidden yet important patterns in data. Discovery of the patterns helps data owners to make right decision to enhance efficiency, increase profit and reduce loss. However, there is privacy concern especially when the data owner is not the miner or when many parties are involved. This research studied privacy preserving association rule mining (PPARM) of horizontally partitioned and outsourced data. Existing research works in the area concentrated mainly on the privacy issue and paid very little attention to data quality issue. Meanwhile, the more the data quality, the more accurate and reliable will the association rules be. Consequently, this research proposed Attribute-Identity Mapping (AIM) as a PPARM technique to address the data quality issue. Given a dataset, AIM identifies set of attributes, attribute values for each attribute. It then assigns ‘unique’ identity for each of the attributes and their corresponding values. It then generates sanitized dataset by replacing each attribute and its values with their corresponding identities. For privacy preservation purpose, the sanitization process will be carried out by data owners. They then send the sanitized data, which is made up of only identities, to data miner. When any or all the data owners need(s) ARM result from the aggregate data, they send query to the data miner. The query constitutes attributes (in form of identities), minSup and minConf thresholds and then number of rules they are want. Results obtained show that the PPARM technique maintains 100% data quality without compromising privacy, using Census Income dataset

Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations

In the growing world of artificial intelligence, federated learning is a distributed learning framework enhanced to preserve the privacy of individuals' data. Federated learning lays the groundwork for collaborative research in areas where the data is sensitive. Federated learning has several implications for real-world problems. In times of crisis, when real-time decision-making is critical, federated learning allows multiple entities to work collectively without sharing sensitive data. This distributed approach enables us to leverage information from multiple sources and gain more diverse insights. This paper is a systematic review of the literature on privacy-preserving machine learning in the last few years based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specifically, we have presented an extensive review of supervised/unsupervised machine learning algorithms, ensemble methods, meta-heuristic approaches, blockchain technology, and reinforcement learning used in the framework of federated learning, in addition to an overview of federated learning applications. This paper reviews the literature on the components of federated learning and its applications in the last few years. The main purpose of this work is to provide researchers and practitioners with a comprehensive overview of federated learning from the machine learning point of view. A discussion of some open problems and future research directions in federated learning is also provided

Models and Algorithms for Private Data Sharing

In recent years, there has been a tremendous growth in the collection of digital information about individuals. Many organizations such as governmental agencies, hospitals, and financial companies collect and disseminate various person-specific data. Due to the rapid advance in the storing, processing, and networking capabilities of the computing devices, the collected data can now be easily analyzed to infer valuable information for research and business purposes. Data from different sources can be integrated and further analyzed to gain better insights. On one hand, the collected data offer tremendous opportunities for mining useful information. On the other hand, the mining process poses a threat to individual privacy since these data often contain sensitive information. In this thesis, we address the problem of developing anonymization algorithms to thwart potential privacy attacks in different real-life data sharing scenarios. In particular, we study two privacy models: LKC-privacy and differential privacy. For each of these models, we develop algorithms for anonymizing different types of data such as relational data, trajectory data, and heterogeneous data. We also develop algorithms for distributed data where multiple data publishers cooperate to integrate their private data without violating the given privacy requirements. Experimental results on the real-life data demonstrate that the proposed anonymization algorithms can effectively retain the essential information for data analysis and are scalable for large data sets

Uncovering the Potential of Federated Learning: Addressing Algorithmic and Data-driven Challenges under Privacy Restrictions

Federated learning is a groundbreaking distributed machine learning paradigm that allows for the collaborative training of models across various entities without directly sharing sensitive data, ensuring privacy and robustness. This Ph.D. dissertation delves into the intricacies of federated learning, investigating the algorithmic and data-driven challenges of deep learning models in the presence of additive noise in this framework. The main objective is to provide strategies to measure the generalization, stability, and privacy-preserving capabilities of these models and further improve them. To this end, five noise infusion mechanisms at varying noise levels within centralized and federated learning settings are explored. As model complexity is a key component of the generalization and stability of deep learning models during training and evaluation, a comparative analysis of three Convolutional Neural Network (CNN) architectures is provided. A key contribution of this study is introducing specific metrics for training with noise. Signal-to-Noise Ratio (SNR) is introduced as a quantitative measure of the trade-off between privacy and training accuracy of noise-infused models, aiming to find the noise level that yields optimal privacy and accuracy. Moreover, the Price of Stability and Price of Anarchy are defined in the context of privacy-preserving deep learning, contributing to the systematic investigation of the noise infusion mechanisms to enhance privacy without compromising performance. This research sheds light on the delicate balance between these critical factors, fostering a deeper understanding of the implications of noise-based regularization in machine learning. The present study also explores a real-world application of federated learning in weather prediction applications that suffer from the issue of imbalanced datasets. Utilizing data from multiple sources combined with advanced data augmentation techniques improves the accuracy and generalization of weather prediction models, even when dealing with imbalanced datasets. Overall, federated learning is pivotal in harnessing decentralized datasets for real-world applications while safeguarding privacy. By leveraging noise as a tool for regularization and privacy enhancement, this research study aims to contribute to the development of robust, privacy-aware algorithms, ensuring that AI-driven solutions prioritize both utility and privacy

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Distributed analysis of vertically partitioned sensor measurements under communication constraints

Nowadays, large amounts of data are automatically generated by devices and sensors. They measure, for instance, parameters of production processes, environmental conditions of transported goods, energy consumption of smart homes, traffic volume, air pollution and water consumption, or pulse and blood pressure of individuals. The collection and transmission of data is enabled by electronics, software, sensors and network connectivity embedded into physical objects. The objects and infrastructure connecting such objects are called the Internet of Things (IoT). In 2010, already 12.5 billion devices were connected to the IoT, a number about twice as large as the world's population at that time. The IoT provides us with data about our physical environment, at a level of detail never known before in human history. Understanding such data creates opportunities to improve our way of living, learning, working, and entertaining. For instance, the information obtained from data analysis modules embedded into existing processes could help their optimization, leading to more sustainable systems which save resources in sectors such as manufacturing, logistics, energy and utilities, the public sector, or healthcare. IoT's inherent distributed nature, the resource constraints and dynamism of its networked participants, as well as the amounts and diverse types of data collected are challenging even the most advanced automated data analysis methods known today. Currently, there is a strong research focus on the centralization of all data in the cloud, processing it according to the paradigm of parallel high-performance computing. However, the resources of devices and sensors at the data generating side might not suffice to transmit all data. For instance, pervasive distributed systems such as wireless sensors networks are highly communication-constrained, as are streaming high throughput applications, or those where data masses are simply too huge to be sent over existing communication lines, like satellite connections. Hence, the IoT requires a new generation of distributed algorithms which are resource-aware and intelligently reduce the amount of data transmitted and processed throughout the analysis chain. This thesis deals with the distributed analysis of vertically partitioned sensor measurements under communication constraints, which is a particularly challenging scenario. Here, not observations are distributed over nodes, but their feature values. The learning of accurate prediction models may require the combination of information from different nodes, necessarily leading to communication. The main question is how to design communication-efficient algorithms for the scenario, while at the same time preserving sufficient accuracy. The first part of the thesis introduces fundamental concepts. An overview of the IoT and its many applications is given, with a special focus on data analysis, the vertically partitioned data scenario, and accompanying research questions. Then, basic notions of machine learning and data mining are introduced. A selection of existing distributed data mining approaches is presented and discussed in more detail. Distributed learning in the vertically partitioned data scenario is then motivated by a smart manufacturing case study. In a hot rolling mill, different machines assess parameters describing the processing of single steel blocks, whose quality should be predicted as early as possible, by analysis of distributed measurements. Each machine creates not single value series, but many of them. Their heterogeneity leads to challenging questions concerning the steps of preprocessing and finding a good representation for learning, for which solutions are proposed. Another problem is that quality information is not given for individual blocks, but charges of blocks. How can we nevertheless predict the quality of individual blocks? Time constraints lead to questions typical for the vertically partitioned data scenario. Which data should be analyzed locally, to match the constraints, and which should be sent to a central server? Learning from aggregated label information is a relatively novel problem in machine learning research. A new algorithm for the task is developed and evaluated, the Learning from Label Proportions by Clustering (LLPC) algorithm. The algorithm's performance is compared to three other state-of-the-art approaches, in terms of accuracy and running time. It can be shown that LLPC achieves results with lower running time, while accuracy is comparable to that of its competitors, or significantly higher. The proposed algorithm comes with many other benefits, like ease of implementation and a small memory footprint. For highly decentralized systems, the Training of Local Models from (Label) Counts (TLMC) algorithm is proposed. The method builds on LLPC, reducing communication by transferring only label counts for batches of observations between nodes. Feasibility of the approach is demonstrated by evaluating the algorithm's performance in the context of traffic flow prediction. It is shown that TLMC is much more communication-efficient than centralization of all data, but that accuracy can nevertheless compete with that of a centrally trained global model. Finally, a communication-efficient distributed algorithm for anomaly detection is proposed, the Vertically Distributed Core Vector Machine (VDCVM). It can be shown that the proposed algorithm communicates up to an order of magnitude less data during learning, in comparison to another state-of-the-art approach, or training a global model by the centralization of all data. Nevertheless, in many relevant cases, the VDCVM achieves similar or even higher accuracy on several controlled and benchmark datasets. A main result of the thesis is that communication-efficient learning is possible in cases where features from different nodes are conditionally independent, given the target value to be predicted. Most efficient are local models, which exchange label information between nodes. In comparison to consensus algorithms, which transmit labels repeatedly, TLMC sends labels only once between nodes. Communication could be even reduced further by learning from counts of labels. In the context of traffic flow prediction, the accuracy achieved is still sufficient in comparison to centralizing all data and training a global model. In the case of anomaly detection, similar results could be achieved by utilizing a sampling approach which draws only as many observations as needed to reach a (1+ε)-approximation of the minimum enclosing ball (MEB). The developed approaches have many applications in communication-constrained settings, in the sectors mentioned above. It has been shown that data can be reduced and learned from before it even enters the cloud. Decentralized processing might thus enable the analysis of big data masses, the more devices are getting connected to the IoT

Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field

WikiSensing: A collaborative sensor management system with trust assessment for big data

Big Data for sensor networks and collaborative systems have become ever more important in the digital economy and is a focal point of technological interest while posing many noteworthy challenges. This research addresses some of the challenges in the areas of online collaboration and Big Data for sensor networks. This research demonstrates WikiSensing (www.wikisensing.org), a high performance, heterogeneous, collaborative data cloud for managing and analysis of real-time sensor data. The system is based on the Big Data architecture with comprehensive functionalities for smart city sensor data integration and analysis. The system is fully functional and served as the main data management platform for the 2013 UPLondon Hackathon. This system is unique as it introduced a novel methodology that incorporates online collaboration with sensor data. While there are other platforms available for sensor data management WikiSensing is one of the first platforms that enable online collaboration by providing services to store and query dynamic sensor information without any restriction of the type and format of sensor data. An emerging challenge of collaborative sensor systems is modelling and assessing the trustworthiness of sensors and their measurements. This is with direct relevance to WikiSensing as an open collaborative sensor data management system. Thus if the trustworthiness of the sensor data can be accurately assessed, WikiSensing will be more than just a collaborative data management system for sensor but also a platform that provides information to the users on the validity of its data. Hence this research presents a new generic framework for capturing and analysing sensor trustworthiness considering the different forms of evidence available to the user. It uses an extensible set of metrics that can represent such evidence and use Bayesian analysis to develop a trust classification model. Based on this work there are several publications and others are at the final stage of submission. Further improvement is also planned to make the platform serve as a cloud service accessible to any online user to build up a community of collaborators for smart city research.Open Acces

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications