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Differential privacy preserving clustering in distributed datasets using Haar wavelet transform

INTELLIGENT DATA ANALYSIS 2015年第6期19卷 1323-1353页

作者： Dishabi, Mohammad Reza Ebrahimi Azgomi, Mohammad Abdollahi Islamic Azad Univ Miyaneh Branch Dept Comp Engn Miyaneh Iran Iran Univ Sci & Technol Sch Comp Engn Tehran *** Iran

The goal of privacy preserving clustering (PPC) is to preserve the privacy of data during clustering analysis. Most of the existing PPC algorithms are based on heuristic notions without provable privacy. Differential privacy is the strong notion of privacy introduced to overcome this problem. However, the lower degree of utility is the serious drawback of the techniques, which preserve differential privacy. In addition, high dimensionality of data is another drawback of the most existing PPC techniques, which leads to low efficiency of them. This paper proposes differential-based algorithms for PPC in horizontally and vertically distributed datasets. To overcome the above two drawbacks, we have used orthogonal discrete wavelet transforms (DWT) for obtaining perturbed data with both low data dimensionality and less noise addition. Our algorithms are implemented and experimented using some well-known datasets. The results show that the proposed algorithms guarantee an appropriate level of both utility and privacy of the published data.

关键词： Differential privacy privacy preserving clustering (PPC) dimension reduction Haar wavelet transform (HWT) distributed datasets

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Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on distributed datasets

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2020年 15卷 3035-3050页

作者： Froelicher, David Troncoso-Pastoriza, Juan Ramon Sousa, Joao Sa Hubaux, Jean-Pierre Ecole Polytech Fed Lausanne Lab Data Secur CH-1015 Lausanne Switzerland Ecole Polytech Fed Lausanne DeDiS Lab CH-1015 Lausanne Switzerland

Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical research, but remains difficult to achieve when the data are sensitive. In fact, sharing personal information requires individuals' unconditional consent or is often simply forbidden for privacy and security reasons. In this paper, we propose Drynx, a decentralized system for privacy-conscious statistical analysis on distributed datasets. Drynx relies on a set of computing nodes to enable the computation of statistics such as standard deviation or extrema, and the training and evaluation of machine-learning models on sensitive and distributed data. To ensure data confidentiality and the privacy of the data providers, Drynx combines interactive protocols, homomorphic encryption, zero-knowledge proofs of correctness, and differential privacy. It enables an efficient and decentralized verification of the input data and of all the system's computations thus provides auditability in a strong adversarial model in which no entity has to be individually trusted. Drynx is highly modular, dynamic and parallelizable. Our evaluation shows that it enables the training of a logistic regression model on a dataset (12 features and 600,000 records) distributed among 12 data providers in less than 2 seconds. The computations are distributed among 6 computing nodes, and Drynx enables the verification of the query execution's correctness in less than 22 seconds.

关键词： Computational modeling Machine learning Encryption Privacy Decentralized system distributed datasets privacy statistics machine learning homomorphic encryption zero-knowledge proofs differential privacy

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A privacy preserving clustering technique for horizontally and vertically distributed datasets

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INTELLIGENT DATA ANALYSIS 2011年第4期15卷 503-532页

作者： Hajian, Sara Azgomi, Mohammad Abdollahi Iran Univ Sci & Technol Sch Comp Engn Tehran *** Iran

Despite the benefits of data mining in a wide range of applications, this technique has raised some issues related to the privacy and security of individuals. Due to these issues, data owners may prevent to share their sensitive information with data miners. On the other hand, in distributed environments, other issues related to the distribution of data will raise, which will make the preservation of privacy more challengeable. To solve these problems, different privacy preserving data mining (PPDM) techniques have been introduced. In this paper, a new privacy preserving clustering (PPC) technique for horizontally and vertically distributed datasets is proposed. The proposed technique uses Haar wavelet transforms (HWT) and scaling data perturbation (SDP) to achieve both data hiding and data reduction for protecting private numerical attribute values in distributed datasets. The results of our evaluations demonstrated that the proposed technique provides a proper degree of privacy and quality of clustering for distributed datasets and also runs fast. Our experiments have also shown that the proposed technique provides better privacy and clustering results comparing to the other existing privacy preserving clustering techniques applicable to distributed datasets. The proposed algorithms and the results of their experimental evaluations using different datasets are presented in this paper.

关键词： Privacy preserving data mining (PPDM) privacy preserving clustering (PPC) distributed datasets Haar wavelet transform (HWT) scaling data perturbation (SDP)

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Cluster based similarity extraction upon distributed datasets

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第3期27卷 2917-2929页

作者： Moustakas, Thanasis Kolomvatsos, Kostas Univ Thessaly Dept Informat & Telecommun Lamia Greece

The internet of things (IoT) offers a vast infrastructure where numerous devices interact to collect data or perform simple processing activities. These devices are, usually, equipped with sensors, software and storage capabilities being able to process the collected data and exchange knowledge with their peers. Data and knowledge may be transferred, in an upwards mode, to the edge infrastructure and Cloud to be the subject of more complex processing. Advanced processing may be also realized upon clusters of IoT devices or edge nodes, i.e., a set of interconnected devices/*** group of nodes may work to achieve a common goal, thus, we may desire to rely on their collected data to support activities like federated learning.A training process, when necessary, may be delivered across the cluster of nodes to conclude the view of the group. In this paper, we focus on the development of a monitoring process upon the collected data and a grouping method upon multiple datasets formulated by IoT devices (hosted by edge nodes) in order to deliver the most similar ones. Our vision is to continuously identify similar datasets to support knowledge extraction through the adoption of techniques like the aforementioned federated learning. We utilize a correlation detection method combined with a probabilistic model to conclude the most similar datasets.

关键词： Similarity extraction Cluster computing distributed datasets Distance function

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A Novel Model for Content Based Video Classification of distributed datasets

A Novel Model for Content Based Video Classification of Dist...

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International Conference on Industrial Instrumentation and Control

作者： Kulkarni, Siddhant Bhatia, Deepti Sabale, Sonali Shinde, Sandhya Pimpri Chinchwad Coll Engn Dept Informat Technol Pune Maharashtra India

ISBN: (纸本)9781479971657

We are now living in the age of Big Data. The amount of data being generated every single second is beyond comprehensible. Human capacity for manual data analysis is nowhere near the rate at which new data is being captured. Video data is one of the major contributors to this data flood. The sheer size of this data makes it impossible to store it on an independent node, let alone classify it. This paper proposes a novel model for classifying large amounts of video data stored on networked nodes. The proposed model uses a multi-system and multi-threaded approach for classifying large amounts of videos while making rigorous use of all the resources at its disposal.

关键词： Content Based Video Classification distributed datasets Multi-threaded Master-Slave Data mining Video Processing

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Privacy-preserving federated machine learning on FAIR health data: A real-world application

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2024年 24卷 136-145页

作者： Sinaci, A. Anil Gencturk, Mert Alvarez-Romero, Celia Erturkmen, Gokce Banu Laleci Martinez-Garcia, Alicia Escalona-Cuaresma, Maria Jose Parra-Calderon, Carlos Luis SRDC Software Res Dev & Consultancy Corp Ankara Turkiye Middle East Tech Univ Dept Comp Engn Ankara Turkiye Univ Seville Virgen del Rocio Univ Hosp Inst Biomed Seville Grp Res & Innovat Biomed Informat Biomed Engn & Hl Seville Spain Univ Seville Languages & Syst Dept Seville Spain SRDC AS ODTU Teknokent Silikon Bina K1-16 TR-06800 Ankara Turkiye

Objective: This paper introduces a privacy-preserving federated machine learning (ML) architecture built upon Findable, Accessible, Interoperable, and Reusable (FAIR) health data. It aims to devise an architecture for executing classification algorithms in a federated manner, enabling collaborative model-building among health data owners without sharing their datasets. Materials and methods: Utilizing an agent-based architecture, a privacy-preserving federated ML algorithm was developed to create a global predictive model from various local models. This involved formally defining the algorithm in two steps: data preparation and federated model training on FAIR health data and constructing the architecture with multiple components facilitating algorithm execution. The solution was validated by five healthcare organizations using their specific health datasets. Results: Five organizations transformed their datasets into Health Level 7 Fast Healthcare Interoperability Resources via a common FAIRification workflow and software set, thereby generating FAIR datasets. Each organization deployed a Federated ML Agent within its secure network, connected to a cloud-based Federated ML Manager. System testing was conducted on a use case aiming to predict 30-day readmission risk for chronic obstructive pulmonary disease patients and the federated model achieved an accuracy rate of 87%. Discussion: The paper demonstrated a practical application of privacy-preserving federated ML among five distinct healthcare entities, highlighting the value of FAIR health data in machine learning when utilized in a federated manner that ensures privacy protection without sharing data. Conclusion: This solution effectively leverages FAIR datasets from multiple healthcare organizations for federated ML while safeguarding sensitive health datasets, meeting legislative privacy and security requirements.

关键词： Privacy -preserving machine learning Federated machine learning FAIR data distributed datasets

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Meta-learning in grid-based data mining systems

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INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND distributed SYSTEMS 2010年第3期5卷 214-228页

作者： Hmida, Moez Ben Haj Slimani, Yahya Fac Sci Tunis Dept Comp Sci Campus Univ Tunis 2092 Tunisia

The Weka4GML framework has been designed to meet the requirements of distributed data mining. In this paper, we present the Weka4GML architecture based on WSRF technology for developing meta-learning methods to deal with datasets distributed among data grid. This framework extends the Weka toolkit to support distributed execution of data mining methods, like meta-learning. The architecture and the behaviour of the proposed framework are described in this paper. We also detail the different steps needed to execute a meta-learning process on a Globus environment. Finally, the framework has been discussed and compared to related works.

关键词： data mining meta-learning grid computing distributed datasets web service resource framework WSRF

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Privacy preserving set intersection protocol secure against malicious behaviors

Privacy preserving set intersection protocol secure against ...

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8th International Conference on Parallel and distributed Computing, Applications and Technologies

作者： Sang, Yingpeng Shen, Hong Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia

ISBN: (纸本)9780769530499

When datasets are distributed on different sources, finding out their intersection while preserving the privacy of the datasets is a widely required task. In this paper we address the Privacy Preserving Set Intersection (PPSI) problem, in which each of the N parties learns no elements other than the intersection of their N private datasets. We propose an efficient protocol in the malicious model, where the adversary may control arbitrary number of parties and execute the protocol for its own benefit. A related work in [12] has a correctness probability of (N-1/N)(N) (N is the size of the encryption scheme's plaintext space), a computation complexity of O(N(2)S(2)lgN) (S is the size of each party's data set). Our PPSI protocol in the malicious model has a correctness probability of (N-1/N)(N-1), and achieves a computation cost of O(c(2)S(2)lgN) (c is the number of malicious parties and c <= N-1).

关键词： cryptographic protocol privacy preservation distributed datasets set intersection zero-knowledge proof

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A Comparative Study on Association Rule Mining in distributed Data Mining 5

A Comparative Study on Association Rule Mining in Distribute...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Waseem, Mohammad Abidin, Shafiqul Aligarh Muslim University Department of Computer Science Aligarh India

ISBN: (纸本)9798350383522

Association rule mining (ARM) is a data mining approach used to identify interesting relationships and patterns in massive datasets. Its objective is to categories associations between various items or variables based on their co-occurrence in the dataset. The fundamental idea behind ARM is to find hidden patterns that can provide important information for prediction, decision-making, and other data analysis tasks. With the rise of big data and the proliferation of distributed computing platforms, there is a need to investigate and compare various methods for doing association rule mining in remote datasets. This paper provides a complete comparative analysis of ARM algorithms and methodologies in distributed systems. We examine the scalability, performance, and efficiency of many cutting-edge algorithms, emphasizing their strengths, limits, and applicability to various types of distributed datasets. This work also provides insights into the present landscape of association rule mining in remote datasets, as well as help researchers and practitioners choose the best algorithms for their particular needs. © 2024 IEEE.

关键词： association rule mining big data Data mining DDM distributed datasets efficiency performance scalability

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