Archives for homomorphic encryption
PySyft decouples private data from model training, using federated learning, differential privacy, multi-party computation (MPC) within the main deep learning framework like PyTorch, Keras and TensorFlow.
The post Difference Between PyTorch And PySyft appeared first on Analytics India Magazine.
PySyft decouples private data from model training, using federated learning, differential privacy, multi-party computation (MPC) within the main deep learning framework like PyTorch, Keras and TensorFlow.
The post Difference Between PyTorch And PySyft appeared first on Analytics India Magazine.
PySyft decouples private data from model training, using federated learning, differential privacy, multi-party computation (MPC) within the main deep learning framework like PyTorch, Keras and TensorFlow.
The post Difference Between PyTorch And PySyft appeared first on Analytics India Magazine.
Data anonymization is the process of stripping all personally identifiable information from the dataset while retaining only the relevant part without compromising the users’ privacy. One of its most important applications is in healthcare. Hospitals often remove patients’ names, addresses, and other vital information from the health records before incorporating them into large datasets. Loopholes…
The post Data Anonymization Is Not A Fool Proof Method: Here’s Why appeared first on Analytics India Magazine.
Data anonymization is the process of stripping all personally identifiable information from the dataset while retaining only the relevant part without compromising the users’ privacy. One of its most important applications is in healthcare. Hospitals often remove patients’ names, addresses, and other vital information from the health records before incorporating them into large datasets. Loopholes…
The post Data Anonymization Is Not A Fool Proof Method: Here’s Why appeared first on Analytics India Magazine.
Data anonymization is the process of stripping all personally identifiable information from the dataset while retaining only the relevant part without compromising the users’ privacy. One of its most important applications is in healthcare. Hospitals often remove patients’ names, addresses, and other vital information from the health records before incorporating them into large datasets. Loopholes…
The post Data Anonymization Is Not A Fool Proof Method: Here’s Why appeared first on Analytics India Magazine.
Companies today are leveraging more and more of user data to build models that improve their products and user experience. Companies are looking to measure user sentiments to develop products as per their need. However, this predictive capability using data can be harmful to individuals who wish to protect their privacy. Building data models using…
The post Top Technologies To Achieve Security And Privacy Of Sensitive Data In AI Models appeared first on Analytics India Magazine.
Machine learning has been revamping the way we handle various day-to-day tasks, but due to data privacy concerns, the proliferation is being impeded. However, Julia computing research team has run machine learning algorithms on encrypted data using cryptographic techniques, which is being touted to preserve privacy. Current Landscape In Machine Learning Machine learning models can…
The post Julia Computing Uses Homomorphic Encryption For ML. Is It The Way Forward? appeared first on Analytics India Magazine.