Cloud based privacy preserving data mining model using hybrid k-anonymity and partial homomorphic encryption

The evolution of information and communication technologies have encourage numerous organizations to outsource their business and data to cloud computing to perform data mining and other data processing operations. Despite the great benefits of the cloud, it has a real problem in the security and privacy of data. Many studies explained that attackers often reveal the information from third-party services or third-party clouds. When a data owners outsource their data to the cloud, especially the SaaS cloud model, it is difficult to preserve the confidentiality and integrity of the data. Privacy-Preserving Data Mining (PPDM) aims to accomplish data mining operations while protecting the owner's data from violation. The current models of PPDM have some limitations. That is, they suffer from data disclosure caused by identity and attributes disclosure where some private information is revealed which causes the success of different types of attacks. Besides, existing solutions have poor data utility and high computational performance overhead. Therefore, this research aims to design and develop Hybrid Anonymization Cryptography PPDM (HAC-PPDM) model to improve the privacy-preserving level by reducing data disclosure before outsourcing data for mining over the cloud while maintaining data utility. The proposed HAC-PPDM model is further aimed reducing the computational performance overhead to improve efficiency. The Quasi-Identifiers Recognition algorithm (QIR) is defined and designed depending on attributes classification and Quasi-Identifiers dimension determine to overcome the identity disclosure caused by Quasi-Identifiers linking to reduce privacy leakage. An Enhanced Homomorphic Scheme is designed based on hybridizing Cloud-RSA encryption scheme, Extended Euclidean algorithm (EE), Fast Modular Exponentiation algorithm (FME), and Chinese Remainder Theorem (CRT) to minimize the computational time complexity while reducing the attribute disclosure. The proposed QIR, Enhanced Homomorphic Scheme and k-anonymity privacy model have been hybridized to obtain optimal data privacy-preservation before outsourced it on the cloud while maintaining the utility of data that meets the needs of mining with good efficiency. Real-world datasets have been used to evaluate the proposed algorithms and model. The experimental results show that the proposed QIR algorithm improved the data privacy-preserving percentage by 23% while maintaining the same or slightly better data utility. Meanwhile, the proposed Enhanced Homomorphic Scheme is more efficient comparing to the related works in terms of time complexity as represented by Big O notation. Moreover, it reduced the computational time of the encryption, decryption, and key generation time. Finally, the proposed HAC-PPDM model successfully reduced the data disclosures and improved the privacy-preserving level while preserved the data utility as it reduced the information loss. In short, it achieved improvement of privacy preserving and data mining (classification) accuracy by 7.59 % and 0.11 % respectively

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

Privacy-Preserving Crowdsourcing-Based Recommender Systems for E-Commerce & Health Services

En l’actualitat, els sistemes de recomanació han esdevingut un mecanisme fonamental per proporcionar als usuaris informació útil i filtrada, amb l’objectiu d’optimitzar la presa de decisions, com per exemple, en el camp del comerç electrònic. La quantitat de dades existent a Internet és tan extensa que els usuaris necessiten sistemes automàtics per ajudar-los a distingir entre informació valuosa i soroll. No obstant, sistemes de recomanació com el Filtratge Col·laboratiu tenen diverses limitacions, com ara la manca de resposta i la privadesa. Una part important d'aquesta tesi es dedica al desenvolupament de metodologies per fer front a aquestes limitacions. A més de les aportacions anteriors, en aquesta tesi també ens centrem en el procés d'urbanització que s'està produint a tot el món i en la necessitat de crear ciutats més sostenibles i habitables. En aquest context, ens proposem solucions de salut intel·ligent (s-health) i metodologies eficients de caracterització de canals sense fils, per tal de proporcionar assistència sanitària sostenible en el context de les ciutats intel·ligents.En la actualidad, los sistemas de recomendación se han convertido en una herramienta indispensable para proporcionar a los usuarios información útil y filtrada, con el objetivo de optimizar la toma de decisiones en una gran variedad de contextos. La cantidad de datos existente en Internet es tan extensa que los usuarios necesitan sistemas automáticos para ayudarles a distinguir entre información valiosa y ruido. Sin embargo, sistemas de recomendación como el Filtrado Colaborativo tienen varias limitaciones, tales como la falta de respuesta y la privacidad. Una parte importante de esta tesis se dedica al desarrollo de metodologías para hacer frente a esas limitaciones. Además de las aportaciones anteriores, en esta tesis también nos centramos en el proceso de urbanización que está teniendo lugar en todo el mundo y en la necesidad de crear ciudades más sostenibles y habitables. En este contexto, proponemos soluciones de salud inteligente (s-health) y metodologías eficientes de caracterización de canales inalámbricos, con el fin de proporcionar asistencia sanitaria sostenible en el contexto de las ciudades inteligentes.Our society lives an age where the eagerness for information has resulted in problems such as infobesity, especially after the arrival of Web 2.0. In this context, automatic systems such as recommenders are increasing their relevance, since they help to distinguish noise from useful information. However, recommender systems such as Collaborative Filtering have several limitations such as non-response and privacy. An important part of this thesis is devoted to the development of methodologies to cope with these limitations. In addition to the previously stated research topics, in this dissertation we also focus in the worldwide process of urbanisation that is taking place and the need for more sustainable and liveable cities. In this context, we focus on smart health solutions and efficient wireless channel characterisation methodologies, in order to provide sustainable healthcare in the context of smart cities

Predictive Modelling Approach to Data-Driven Computational Preventive Medicine

This thesis contributes novel predictive modelling approaches to data-driven computational preventive medicine and offers an alternative framework to statistical analysis in preventive medicine research. In the early parts of this research, this thesis presents research by proposing a synergy of machine learning methods for detecting patterns and developing inexpensive predictive models from healthcare data to classify the potential occurrence of adverse health events. In particular, the data-driven methodology is founded upon a heuristic-systematic assessment of several machine-learning methods, data preprocessing techniques, models’ training estimation and optimisation, and performance evaluation, yielding a novel computational data-driven framework, Octopus. Midway through this research, this thesis advances research in preventive medicine and data mining by proposing several new extensions in data preparation and preprocessing. It offers new recommendations for data quality assessment checks, a novel multimethod imputation (MMI) process for missing data mitigation, a novel imbalanced resampling approach, and minority pattern reconstruction (MPR) led by information theory. This thesis also extends the area of model performance evaluation with a novel classification performance ranking metric called XDistance. In particular, the experimental results show that building predictive models with the methods guided by our new framework (Octopus) yields domain experts' approval of the new reliable models’ performance. Also, performing the data quality checks and applying the MMI process led healthcare practitioners to outweigh predictive reliability over interpretability. The application of MPR and its hybrid resampling strategies led to better performances in line with experts' success criteria than the traditional imbalanced data resampling techniques. Finally, the use of the XDistance performance ranking metric was found to be more effective in ranking several classifiers' performances while offering an indication of class bias, unlike existing performance metrics The overall contributions of this thesis can be summarised as follow. First, several data mining techniques were thoroughly assessed to formulate the new Octopus framework to produce new reliable classifiers. In addition, we offer a further understanding of the impact of newly engineered features, the physical activity index (PAI) and biological effective dose (BED). Second, the newly developed methods within the new framework. Finally, the newly accepted developed predictive models help detect adverse health events, namely, visceral fat-associated diseases and advanced breast cancer radiotherapy toxicity side effects. These contributions could be used to guide future theories, experiments and healthcare interventions in preventive medicine and data mining

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

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

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model

© 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum