What Is the Price for Joining Securely? Benchmarking Equi-Joins in Trusted Execution Environments

Summary: Benchmarks eight equi-join algorithms in TEEs using TEEbench; first comprehensive TEEs join study highlighting bottlenecks due to sealing/obliviousness. Findings: up to 3 orders of magnitude slower than plain CPUs; calls for hardware-aware redesign and secure query plan tuning. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 13127
Venue: VLDB
Year: 2022
Pagerank: 5.8246671e-05
Overall Rank: 6,601 | 54.72%
DOI: 10.14778/3494124.3494146

Incoming Non-self Citations Over Time

Authors

1. Kajetan Maliszewski (Technical University of Berlin)
2. Jorge-Arnulfo Quiané-Ruiz (German National Research Center for Information Technology; Technical University of Berlin)
3. Jonas Traub (Technical University of Berlin)
4. Volker Markl (German National Research Center for Information Technology; Technical University of Berlin)

BibTeX Citation

@article{maliszewski_vldb22,
        title = {{What Is the Price for Joining Securely? Benchmarking Equi-Joins in Trusted Execution Environments}},
        author = {Maliszewski, Kajetan and Quiané-Ruiz, Jorge-Arnulfo and Traub, Jonas and Markl, Volker},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {3},
        pages = {659--672},
        doi = {10.14778/3494124.3494146},
        url = {https://doi.org/10.14778/3494124.3494146},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,183	Relational Algorithms for Top-k Query Evaluation	2024	SIGMOD	5.093636e-05
11,437	Cracking-Like Join for Trusted Execution Environments	2023	VLDB	5.093636e-05
11,438	Olive: Oblivious Federated Learning on Trusted Execution Environment Against the Risk of Sparsification	2023	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 15 of 15 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
7	Implementation Techniques For Main Memory Database Systems	1984	SIGMOD	0.00083340894
29	Database Architecture Optimized for the New Bottleneck: Memory Access	1999	VLDB	0.00052093615
209	Sort vs. Hash Revisited: Fast Join Implementation on Modern Multi-Core CPUs	2009	VLDB	0.00024932174
252	Multi-Core, Main-Memory Joins: Sort vs. Hash Revisited	2014	VLDB	0.00023242719
360	Design and Evaluation of Main Memory Hash Join Algorithms for Multi-core CPUs	2011	SIGMOD	0.00020182846
712	Efficient Implementation of Sorting on Multi-Core SIMD CPU Architecture	2008	VLDB	0.0001468812
959	Memory-Efficient Hash Joins	2015	VLDB	0.00012953588
1,265	An Experimental Comparison of Thirteen Relational Equi-Joins in Main Memory	2016	SIGMOD	0.00011415709
1,424	Azure SQL Database Always Encrypted	2020	SIGMOD	0.00010819191
2,025	Data Market Platforms: Trading Data Assets to Solve Data Problems	2020	VLDB	9.2903125e-05
2,045	Efficient Oblivious Database Joins	2020	VLDB	9.2632279e-05
3,295	Lightning Fast and Space Efficient Inequality Joins	2015	VLDB	7.5477715e-05
3,464	Building Enclave-Native Storage Engines for Practical Encrypted Databases	2021	VLDB	7.3916399e-05
3,611	NADEEF/ER: Generic and Interactive Entity Resolution	2014	SIGMOD	7.2580469e-05
7,820	Parallelizing Intra-Window Join on Multicores: An Experimental Study	2021	SIGMOD	5.5373345e-05

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