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Privacy impacts every stakeholder in the AI solution ecosystem, including consumers, operators, solution providers and regulators. This is especially true for applications such as healthcare, safety and finance which require collecting and analyzing highly sensitive data. We develop AI solutions to assure customers that private information is not leaked at any stage of the data lifecycle. Our differential privacy method guarantees that an adversary cannot decipher training data from model outputs. Our differential privacy method provides a provable guarantee of privacy, while using significantly less data than competitors. We also develop federated training methods that securely combine private data from multiple users or enterprises, at orders of magnitude lower communication costs than competitors, while providing a guarantee of low leakage through differential privacy.
Team Members: Francesco Pittaluga
Publication Tags: privy, federated learning, computer vision, deep learning, differential privacy, visual privacy protection
Data augmentation techniques, such as image transformations and combinations, are highly effective at improving the generalization of computer vision models, especially when training data is limited. However, such techniques are fundamentally incompatible with differentially private learning approaches,
While deep face recognition has benefited significantly from large-scale labeled data, current research is focused on leveraging unlabeled data to further boost performance, reducing the cost of human annotation. Prior work has mostly been in controlled settings, where the labeled and unlabeled data
With increasing ethical and legal concerns on privacy for deep models in visual recognition, differential privacy has emerged as a mechanism to disguise membership of sensitive data in training datasets. Recent methods like Private Aggregation of Teacher Ensembles (PATE) leverage a large ensemble of