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Efficient Confidentiality-Preserving Data Analytics over Symmetrically Encrypted Datasets

Summary: Two symmetric additive and multiplicative homomorphic schemes enable confidentiality-preserving analytics on encrypted data. Security is proven; they beat asymmetric PHE by exchanging ciphertext compactness for practical relative compactness, with 7x speedups. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12043
Venue
VLDB
Year
2020
Pagerank
5.4232012e-05
Overall Rank
5,584 | 61.16%
DOI
10.14778/3389133.3389144

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

Rank Cited Paper Year Venue Pagerank
66 Spark SQL: Relational Data Processing in Spark 2015 SIGMOD 0.00061639801
459 Processing Analytical Queries over Encrypted Data 2013 VLDB 0.00022627746
973 Orthogonal Security With Cipherbase 2013 CIDR 0.00014921633
1,906 Arx: An Encrypted Database using Semantically Secure Encryption 2019 VLDB 0.00010137764
2,087 Answering Aggregation Queries in a Secure System Model 2007 VLDB 9.5732194e-05
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