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differential privacy
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Explore more → **differential privacy**
What is differential privacy?
Differential privacy is a system for publicly sharing information about a dataset by describing the patterns of groups within the dataset while withholding information about individuals in the dataset. This is achieved by adding carefully calibrated noise to the data or the query results. It is commonly used to analyze and release aggregate statistical data while protecting individual privacy, such as in census data or location data analysis.
What other technologies are related to differential privacy?
differential privacy Competitor Technologies
homomorphic encryption
Homomorphic encryption allows computation on encrypted data without decryption, offering a different approach to data privacy compared to differential privacy.
secure multi-party computation
Secure multi-party computation enables multiple parties to jointly compute a function over their inputs while keeping those inputs private. This is an alternative approach to achieving data privacy, similar to homomorphic encryption, and fulfills similar goals to differential privacy.
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differential privacy Complementary Technologies
federated learning
Federated learning trains a model across multiple decentralized edge devices or servers holding local data samples, without exchanging them. Differential privacy can be applied in federated learning to add noise to the model updates, enhancing privacy.
PyTorch
PyTorch is a machine learning framework that can be used to implement models with differential privacy. Libraries like Opacus facilitate the integration of differential privacy into PyTorch models.
TensorFlow
TensorFlow is a machine learning framework that can be used to implement models with differential privacy. TensorFlow Privacy library supports differential privacy for TensorFlow models.
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Which job functions commonly mention differential privacy?
Research Scientist
0.1% of all Research Scientist jobs mention differential privacy
View 51
jobs on Sumble
Applied Scientist
0.1% of all Applied Scientist jobs mention differential privacy
View 9
jobs on Sumble
Machine Learning
0.1% of all Machine Learning jobs mention differential privacy
View 56
jobs on Sumble
Researcher
101 Researcher jobs mention differential privacy
View 101
jobs on Sumble
Data, Analytics & Machine Learning
128 Data, Analytics & Machine Learning jobs mention differential privacy
View 128
jobs on Sumble
Which organizations are mentioning differential privacy?
Google
Scientific and Technical Services
Microsoft
Scientific and Technical Services
Apple
Scientific and Technical Services
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