Database method improves conflict resolution using typed row confidence
Epistemic Typing as a PostgreSQL Table Access Method: Adversarial Conflict Resolution Under Confidence Forgery and Sybil Coordination
DatabasesCryptography and Security
Summary
Databases sometimes store conflicting information from different sources, making it hard to know what is true. The authors built a system called KNDB that labels each piece of data with a type based on how it was obtained and uses this to resolve conflicts better than existing methods. Their approach is especially good when attackers try to fool the system with false high-confidence data. KNDB works well on real-world datasets where many opinions or data reports must be combined, but it struggles in cases where the most recent update is the ground truth.
What this means in practice
- •For database engineers: Implement conflict resolution in databases that combine data from multiple sources using typed confidence to improve truth discovery under adversarial conditions.
- •For data integration teams: Improve merging of conflicting data from crowdsourced or multi-source environments by using row type metadata to better weigh and resolve discrepancies.
Authors
Venkata Maguluri, Swapna Kommuri, Kritica Sinha, Ashish Sood
Abstract
We describe KNDB, a PostgreSQL 18 table access method (TAM) that types every row with an engine-assigned epistemic kind (MEASURED, INFERRED, or DERIVED) and resolves per-slot conflicts inside every write-time heapam callback. Rows land as ordinary heap tuples; seven of the 44 TAM callbacks are overridden (tuple_insert, multi_insert, tuple_update, tuple_delete, tuple_insert_speculative, tuple_complete_speculative, relation_toast_am), the other 37 delegate to heap; we provide a completeness argument over the interface as a paper artefact. This paper reports the engineering behind that decision and the adversarial evaluation that motivated it. On a confidence-forgery workload where an attacker asserts INFERRED writes with confidence in [0.95,1.0] against honest MEASURED writes with confidence in [0.5,0.9], KNDB beats a confidence-only baseline by 63 percentage points on the Book-Author fusion dataset and 92.7 points on the Zheng crowdsourcing dataset. Both wins are proven load-bearing on the kind axis by a source-rebuild disable-and-test in which the lattice is neutralised and the win vanishes. Against four truth-discovery baselines (TruthFinder, CRH, CATD, ACCU) reimplemented from the original equations and validated to within 0.3 percentage points of the published numbers, KNDB is competitive below a per-dataset density-saturation cell and dominant at or above it. We formalise the cell as k* ~ rho_alg * h_top, where h_top is per-slot top honest surface-form support, and validate the prediction within +/-20% on Book-Author and +/-30% on Zheng. Because the kind axis is assigned by the engine from independent metadata and cannot be forged at write time, KNDB's k* is unbounded. The paper is honest about where KNDB loses: CRH and ACCU outperform KNDB below saturation on Zheng, and KNDB scores zero on three temporal knowledge-editing benchmarks whose ground truth is last-writer-wins.