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Efficient Confidentiality-Preserving Data Analytics over Symmetrically Encrypted Datasets

Summary: Introduces symmetric additive and multiplicative PHE schemes for expressive encrypted analytics, trading strict ciphertext compactness for practical efficiency. Symmetria outperforms asymmetric PHE systems by up to 7× while preserving confidentiality. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12231
Venue
VLDB
Year
2020
Pagerank
6.1348507e-05
Overall Rank
5,649 | 61.25%
DOI
10.14778/3389133.3389144

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{savvides_vldb20,
        title = {{Efficient Confidentiality-Preserving Data Analytics over Symmetrically Encrypted Datasets}},
        author = {Savvides, Savvas and Khandelwal, Darshika and Eugster, Patrick},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {8},
        pages = {1290--1303},
        doi = {10.14778/3389133.3389144},
        url = {https://doi.org/10.14778/3389133.3389144},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

Rank Cited Paper Year Venue Pagerank
24 Spark SQL: Relational Data Processing in Spark 2015 SIGMOD 0.00054865648
412 Processing Analytical Queries over Encrypted Data 2013 VLDB 0.00018901853
876 Orthogonal Security With Cipherbase 2013 CIDR 0.00013467554
1,615 Arx: An Encrypted Database using Semantically Secure Encryption 2019 VLDB 0.00010214058
2,292 Answering Aggregation Queries in a Secure System Model 2007 VLDB 8.7961091e-05
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