Privacy-preserving similarity joins over encrypted data
Similarity search on high-dimensional data has been intensively studied for data processing
and analytics. Despite its broad applicability, data security and privacy concerns along the …
and analytics. Despite its broad applicability, data security and privacy concerns along the …
Scalable and robust set similarity join
Set similarity join is a fundamental and well-studied database operator. It is usually studied
in the exact setting where the goal is to compute all pairs of sets that exceed a given …
in the exact setting where the goal is to compute all pairs of sets that exceed a given …
On the complexity of inner product similarity join
A number of tasks in classification, information retrieval, recommendation systems, and
record linkage reduce to the core problem of inner product similarity join (IPS join) …
record linkage reduce to the core problem of inner product similarity join (IPS join) …
Output-optimal massively parallel algorithms for similarity joins
Parallel join algorithms have received much attention in recent years due to the rapid
development of massively parallel systems such as MapReduce and Spark. In the database …
development of massively parallel systems such as MapReduce and Spark. In the database …
Systems and methods for privacy-assured similarity joins over encrypted datasets
Abstract Systems and methods which provide secure queries with respect to encrypted
datasets are described. Embodiments provide privacy-assured similarity join techniques …
datasets are described. Embodiments provide privacy-assured similarity join techniques …
Output-optimal parallel algorithms for similarity joins
Parallel join algorithms have received much attention in recent years, due to the rapid
development of massively parallel systems such as MapReduce and Spark. In the database …
development of massively parallel systems such as MapReduce and Spark. In the database …
I/O-efficient similarity join
We present an I/O-efficient algorithm for computing similarity joins based on locality-
sensitive hashing (LSH). In contrast to the filtering methods commonly suggested our …
sensitive hashing (LSH). In contrast to the filtering methods commonly suggested our …
Adaptive mapreduce similarity joins
S McCauley, F Silvestri - Proceedings of the 5th ACM SIGMOD …, 2018 - dl.acm.org
Similarity joins are a fundamental database operation. Given data sets S and R, the goal of a
similarity join is to find all points x∈ S and y∈ R with distance at most r. Recent research …
similarity join is to find all points x∈ S and y∈ R with distance at most r. Recent research …
Set similarity search for skewed data
Set similarity join, as well as the corresponding indexing problem set similarity search, are
fundamental primitives for managing noisy or uncertain data. For example, these primitives …
fundamental primitives for managing noisy or uncertain data. For example, these primitives …
Massively-parallel similarity join, edge-isoperimetry, and distance correlations on the hypercube
P Beame, C Rashtchian - Proceedings of the Twenty-Eighth Annual ACM …, 2017 - SIAM
We study distributed protocols for finding all pairs of similar vectors in a large dataset. Our
results pertain to a variety of discrete metrics, and we give concrete instantiations for …
results pertain to a variety of discrete metrics, and we give concrete instantiations for …