Privacy Preserving Burst Detection of Distributed Time Series Data Using Linear Transforms

— In this paper, we consider burst detection within the context of privacy. In our scenario, multiple parties want to detect a burst in aggregated time series data, but none of the parties want to disclose their individual data. Our approach calculates bursts directly from linear transform coefficients using a cumulative sum calculation. In order to reduce the chance of a privacy breech, we present multiple data perturbation strategies and compare the varying degrees of privacy preserved. Our strategies do not share raw time series data and still detect significant bursts. We empirically demonstrate this using both real and synthetic distributed data sets. When evaluating both privacy guarantees and burst detection accuracy, we find that our percentage thresholding heuristic maintains a high degree of privacy while accurately identifying bursts of varying widths. I. MOTIVATION In this paper we consider privacy preservation issues in distributed time series data sets. Sensor data, ...

• Type: Conference
• Year: 2007
• Where: CIDM
• Authors: Lisa Singh, Mehmet Sayal
• Keywords: Artificial Intelligence, CIDM 2007, Data Sets, Series Data Sets, Time Series Data