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Side-channel attacks leverage implementation of algorithms to bypass security and leak restricted data. A timing attack observes differences in runtime in response to varying inputs to learn restricted information. Most prior work has focused on applying timing attacks to cryptoanalysis algorithms; other approaches sought to learn about database content by measuring the time of an operation (e.g., index update or query caching). Our goal is to evaluate the practical risks of leveraging a non-privileged user account to learn about data hidden from the user account by access control. As with other side-channel attacks, this attack exploits the inherent nature of how queries are executed in a database system. Internally, the database engine processes the entire database table, even if the user only has access to some of the rows. We present a preliminary investigation of what a regular user can learn about “hidden” data by observing the execution time of their queries over an indexed column in a table. We perform our experiments in a cache-control environment (i.e., clearing database cache between runs) to measure an upper bound for data leakage and privacy risks. Our experiments show that, in a real system, it is difficult to reliably learn about restricted data due to natural operating system (OS) runtime fluctuations and OS-level caching. However, when the access control mechanism itself is relatively costly, a user can not only learn about hidden data but they may closely approximate the number of rows hidden by the access control mechanism.
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A Community Cache with Complete Information
Kariz is a new architecture for caching data from datalakes accessed, potentially concurrently, by multiple analytic platforms. It integrates rich information from analytics platforms with global knowledge about demand and resource availability to enable sophisticated cache management and prefetching strategies that, for example, combine historical run time information with job dependency graphs (DAGs), information about the cache state and sharing across compute clusters. Our prototype supports multiple analytic frameworks (Pig/Hadoop and Spark), and we show that the required changes are modest. We have implemented three algorithms in Kariz for optimizing the caching of individual queries (one from the literature, and two novel to our platform) and three policies for optimizing across queries from, potentially, multiple different clusters. With an algorithm that fully exploits the rich information available from Kariz, we demonstrate major speedups (as much as 3×) for TPC-H and TPC-DS.
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- Award ID(s):
- 1910327
- PAR ID:
- 10313697
- Date Published:
- Journal Name:
- 19th USENIX Conference on File and Storage Technologies (FAST 21)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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