Archives for differential privacy
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.
“Google AI’s new research on language models, in collaboration with OpenAI and Apple hints that the company cares about transparency.” As language models continue to advance, chances of encountering new and unexpected risks are high. The line of work resembles that of ex-Googler Timnit Gebru who was fired earlier this month for her “inconsistent” allegations…
The post Amid Ethical AI Controversy, Google Releases Research In Collaboration With Apple & OpenAI appeared first on Analytics India Magazine.