Precise and eloquent label information is fundamental for interpreting the underlying data distributions distinctively and training of supervised and semi-supervised learning models adequately. But obtaining large amount of labeled data demands substantial manual effort. This obligation can be mitigated by acquiring labels of most informative data instances using Active Learning. However labels received from humans are not always reliable and poses the risk of introducing noisy class labels which will degrade the efficacy of a model instead of its improvement. In this paper, we address the problem of annotating sensor data instances of various Activities of Daily Living (ADLs) in smart home context. We exploit the interactions between the users and annotators in terms of relationships spanning across spatial and temporal space which accounts for an activity as well. We propose a novel annotator selection model SocialAnnotator which exploits the interactions between the users and annotators and rank the annotators based on their level of correspondence. We also introduce a novel approach to measure this correspondence distance using the spatial and temporal information of interactions, type of the relationships and activities. We validate our proposed SocialAnnotator framework in smart environments achieving ≈ 84% statistical confidence in data annotation
This content will become publicly available on October 1, 2023
Machine Learning with Differentially Private Labels: Mechanisms and Frameworks
Label differential privacy is a relaxation of differential privacy for machine learning scenarios where the labels are the only sensitive information that needs to be protected in the training data. For example, imagine a survey from a participant in a university class about their vaccination status. Some attributes of the students are publicly available but their vaccination status is sensitive information and must remain private. Now if we want to train a model that predicts whether a student has received vaccination using only their public information, we can use label-DP. Recent works on label-DP use different ways of adding noise to the labels in order to obtain label-DP models. In this work, we present novel techniques for training models with label-DP guarantees by leveraging unsupervised learning and semi-supervised learning, enabling us to inject less noise while obtaining the same privacy, therefore achieving a better utility-privacy trade-off. We first introduce a framework that starts with an unsupervised classifier f0 and dataset D with noisy label set Y , reduces the noise in Y using f0 , and then trains a new model f using the less noisy dataset. Our noise reduction strategy uses the model f0 to remove the noisy labels more »
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- Proceedings on Privacy Enhancing Technologies
- Page Range or eLocation-ID:
- 332 to 350
- Sponsoring Org:
- National Science Foundation
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