Project Background:
The privacy-preserving clustering addresses the growing concern over protecting sensitive information while extracting valuable insights from data. Traditional clustering algorithms operate on raw data, raising privacy concerns when dealing with sensitive personal or proprietary information. In response, privacy-preserving clustering methods balance data utility and privacy protection by incorporating cryptographic techniques, anonymization methods, and differential privacy principles. By preserving the confidentiality of individual data points while still enabling meaningful analysis at the aggregate level, these methods ensure compliance with privacy regulations and ethical standards. Moreover, the project seeks to address the inherent trade-off between privacy and utility by developing innovative algorithms that optimize clustering performance while minimizing the disclosure risk of sensitive information.
Problem Statement
- Traditional clustering algorithms typically operate directly on raw data, which may contain sensitive or confidential information, posing privacy risks if not adequately protected.
-
Organizations must comply with privacy regulations such as GDPR, HIPAA, and CCPA, which require safeguarding individuals privacy rights when handling personal data, including during clustering tasks.
-
Anonymizing data to protect privacy while preserving utility for clustering poses challenges, as traditional anonymization methods may degrade the quality and effectiveness of clustering results.
-
Clustering results may inadvertently reveal sensitive information about individuals or groups, especially in datasets with high-dimensional or heterogeneous attributes, increasing the risk of privacy breaches.
-
Balancing the need for privacy with the utility of clustering results requires developing privacy-preserving techniques that maintain the quality and accuracy of clustering while minimizing the disclosure risk of sensitive information.
-
Integrating differential privacy principles into clustering algorithms to provide strong privacy guarantees while allowing for meaningful data analysis at the aggregate level.
-
Establishing appropriate evaluation metrics and benchmarks for assessing the effectiveness and privacy-preserving capabilities of clustering algorithms, considering both clustering quality and privacy protection aspects.
Aim and Objectives
- Develop privacy-preserving clustering techniques that ensure the confidentiality of sensitive data while maintaining the utility of clustering results.
-
Design innovative clustering algorithms incorporating cryptographic techniques and anonymization methods to protect sensitive information.
-
Integrate principles of differential privacy into clustering algorithms to provide strong privacy guarantees.
-
Develop scalable privacy-preserving clustering methods capable of handling large and heterogeneous datasets.
-
Evaluate the effectiveness and utility of privacy-preserving clustering techniques using appropriate metrics and benchmarks.
- Ensure compliance with privacy regulations and ethical standards while deriving meaningful insights from data.
Contributions to Privacy-Preserving Clustering
- Developing innovative algorithms that safeguard sensitive information during clustering, ensuring compliance with privacy regulations and ethical standards.
- Balancing privacy and utility by maintaining the quality and accuracy of clustering results while minimizing the disclosure risk of sensitive data.
-
Integrating differential privacy principles into clustering algorithms to provide strong privacy guarantees while enabling meaningful data analysis at the aggregate level.
- Providing scalable privacy-preserving clustering methods capable of handling large and complex datasets commonly encountered in real-world applications.
- Establishing appropriate evaluation metrics and benchmarks for assessing the effectiveness and privacy-preserving capabilities of clustering algorithms, ensuring rigorous validation of proposed methods.
Deep Learning Algorithms for Privacy-Preserving Clustering
- Deep Neural Network (DNN) with Homomorphic Encryption
-
Deep Autoencoder-based Clustering with Differential Privacy
-
Variational Autoencoder (VAE) with Secure Multiparty Computation
-
Generative Adversarial Network (GAN) for Privacy-Preserving Clustering
-
Deep Recurrent Autoencoder with Homomorphic Encryption
-
Deep Belief Network (DBN) with Secure Aggregation
-
Recurrent Neural Network (RNN) with Federated Learning
-
Deep Reinforcement Learning for Privacy-Preserving Clustering
-
Deep Convolutional Autoencoder with Differential Privacy
-
Deep Restricted Boltzmann Machine (RBM) with Secure Aggregation
Datasets for Privacy-Preserving Clustering
- Adult Census Income Dataset
-
Credit Card Fraud Detection Dataset
-
Healthcare Dataset with Medical Records
-
Mobile Phone Call Data Records
-
Synthetic Dataset with Sensitive Attributes
-
Retail Customer Transaction Data
-
Social Media User Interaction Data
-
Telecom Customer Churn Dataset
-
Web Browsing History Dataset
-
IoT Sensor Data Streams
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1.Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2.Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
