Nov 22, 2021 · Sensitive privacy critically depends on the underlying anomaly model. We develop a novel n-step lookahead mechanism to efficiently answer ...
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Abstract—Identifying anomalies in data is vital in many domains, including medicine, finance, and national security. However, privacy.
Sensitive privacy critically depends on the underlying anomaly model. We develop a novel n-step lookahead mechanism to efficiently answer arbitrary outlier ...
Abstract—Identifying anomalies in data is vital in many domains, including medicine, finance, and national security. However, privacy.
Estimating these explainability changes will allow us to assess the impact of the privacy-preserving mechanisms on the reasoning behind the identified anomalies ...
Feb 16, 2024 · PDF | In this paper a notion of privacy-anomaly detection is presented where normative privacy is modelled using k-anonymity.
We make four contributions. First, we introduce the notion of sensitive privacy, which conceptualizes what it means to privately identify anomalies. Sensitive ...
Our experimental results show that the anomaly detection models can identify various anomalous events even when the training data is transformed with white ...
Anomaly detection is a crucial aspect of fraud prevention, involving the identification of unusual patterns or behaviours within datasets. These anomalies often ...
Anomaly detection is examining specific data points and detecting rare occurrences that seem suspicious because they're different from the established ...
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