Dpod: differentially private outsourcing of anomaly detection

A data analyst node, comprising:

• processing circuitry configured to:
• cause transmission of an anomaly estimation for a first privatized dataset, the first privatized dataset being based on dataset and a first noise profile;
• receive a second privatized dataset that is based on the dataset and a second noise profile different from the first noise profile, the second noise profile being based at least on the anomaly estimation;
• and
• perform anomaly estimation for the second privatized dataset.

A method implemented by a data node, the method comprising:

• receiving an anomaly estimation for a first privatized dataset, the first private dataset being based on a dataset and a first noise profile;
• applying a second noise profile to the dataset to generate a second privatized dataset, the second noise profile being based at least on the anomaly estimation;
• and
• causing transmission of the second privatized dataset for anomaly estimation.

A method implemented by a data analyst node, the method comprising:

• causing transmission of an anomaly estimation for a first privatized dataset, the first privatized dataset being based on dataset and a first noise profile;
• receiving a second privatized dataset that is based on the dataset and a second noise profile different from the first noise profile, the second noise profile being based at least on the anomaly estimation;
• and
• performing anomaly estimation for the second privatized dataset.