HADES: Range-Filtered Private Aggregation on Public Data
Summary: HADES provides one-round FHE aggregation over public data while hiding query predicates, supporting point/range filters and Boolean combinations without trusted hardware or non-colluding servers. Plaintext-aware indicators and optimized reduction yield 204–6574× speedups, aggregating 1M records in 38 seconds. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Xiaoyuan Liu (University of California Berkeley)
- 2. Ni Trieu (Arizona State University)
- 3. Trinabh Gupta (University of California Santa Barbara)
- 4. Ishtiyaque Ahmad (University of California Santa Cruz)
- 5. Dawn Song (University of California Berkeley)
BibTeX Citation
@article{liu_vldb25,
title = {{HADES: Range-Filtered Private Aggregation on Public Data}},
author = {Liu, Xiaoyuan and Trieu, Ni and Gupta, Trinabh and Ahmad, Ishtiyaque and Song, Dawn},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {7},
pages = {2159--2171},
doi = {10.14778/3734839.3734852},
url = {https://doi.org/10.14778/3734839.3734852},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,131 | HEDA: Multi-Attribute Unbounded Aggregation over Homomorphically Encrypted Database | 2023 | VLDB | 7.7249939e-05 |
| 8,640 | Pantheon: Private Retrieval from Public Key-Value Store | 2023 | VLDB | 5.3944963e-05 |
Previous
Page 1 / 1
Next