Grants and Contributions:

Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019).

Secure key/value stores can be used to store a large amount of data in a central place while providing cell-levelx000D
access control to protect data privacy (i.e., each key-value pair has its own security label). This is often veryx000D
attractive to many domains (e.g., healthcare, government) that have a strong requirement for data security andx000D
privacy. However, a key limitation that stops secure key/value stores from being widely adopted is that theyx000D
cannot support analytical queries very efficiently.x000D
To overcome the limitation, in this proposal, we will collaborate with PHEMI to build an approximate queryx000D
processing (AQP) engine over secure key/value stores. The key insight is to allow users to run analyticalx000D
queries on a sample data and return them approximate answers with error bars. Since the queries only need tox000D
be executed on a sample, the query response time can be significantly improved. We will work together withx000D
PHEMI to address two challenging research problems: (1) how to efficiently create a random sample of thex000D
data stored in key/value stores; (2) how to efficiently obtain approximate answers from a sample. To the best ofx000D
our knowledge, we are the first to explore approximate query processing over secure key/value stores.x000D
The PI has many years of successful experience in AQP system development. PHEMI is a big data warehousex000D
startup headquartered in Canada. They use a secure key-value store called Accumulo as the main data store ofx000D
their products. We anticipate that the collaborative research project will not only be beneficial to PHEMI'sx000D
products but also have a profound impact on how to architect a real-world AQP system.