Locality Sensitivity Hashing (LSH) is a powerful approach to similarity (or distance) estimation, which exploits a family of randomized hash functions to map the similar data instances to the same buckets with a higher probability than the dissimilar ones. In other words, LSH algorithms produce comparable hash values for similar values unlike the cryptographic hash algorithms that produces entirely different outputs for similar values.