On the Privacy of Selection Mechanisms with Gaussian Noise
Abstract Report Noisy Max and Above Threshold are two classical differentially private (DP)
selection mechanisms. Their output is obtained by adding noise to a sequence of low …
selection mechanisms. Their output is obtained by adding noise to a sequence of low …
Locally private online change point detection
We study online change point detection problems under the constraint of local differential
privacy (LDP) where, in particular, the statistician does not have access to the raw data. As a …
privacy (LDP) where, in particular, the statistician does not have access to the raw data. As a …
Privacy-Preserving Line Outage Detection in Distribution Grids: An Efficient Approach with Uncompromised Performance
Recent advancements in research have shown the efficacy of employing sensor
measurements, such as voltage and power data, in identifying line outages within …
measurements, such as voltage and power data, in identifying line outages within …
Utility-Aware Time Series Data Release with Anomalies under TLDP
With the prevalence of mobile computing, mobile devices have been generating numerous
sensor data, aka, time series. Since these time series may include sensitive information …
sensor data, aka, time series. Since these time series may include sensitive information …
Private sequential hypothesis testing for statisticians: Privacy, error rates, and sample size
The sequential hypothesis testing problem is a class of statistical analyses where the
sample size is not fixed in advance. Instead, the decision-process takes in new observations …
sample size is not fixed in advance. Instead, the decision-process takes in new observations …