  In collaborative outlier detection, multiple participants exchange their
local detectors trained on decentralized devices without exchanging their own
data. A key problem of collaborative outlier detection is efficiently
aggregating multiple local detectors to form a global detector without
breaching the privacy of participants' data and degrading the detection
accuracy. We study locality-sensitive hashing-based ensemble methods to detect
collaborative outliers since they are mergeable and compatible with
differentially private mechanisms. Our proposed LSH iTables is simple and
outperforms recent ensemble competitors on centralized and decentralized
scenarios over many real-world data sets.
