Data-Centric and Privacy-Preserving Methods
DP-FT
In brief — site editorial, not from the paper
Fine-tunes with mathematical privacy guarantees, bounding how much any single training record can influence the resulting model.
Definition
DP-FT, using DP-SGD, incorporates gradient clipping and noise injection during training to provide mathematical guarantees against membership inference and data extraction attacks. DP-FT is a Parametric Update dedicated exclusively to the Privacy Preservation goal. Unlike FL, which achieves privacy via Distributed data sovereignty, DP-FT operates on centralized data (Large Labeled, Small Labeled) but mathematically protects the records. Like standard parametric updates, DP-FT produces a permanently modified weight checkpoint that can be deployed on a planned cadence or as an event-driven patch, assigning it Ad-hoc Permanent, Scheduled Permanent. It can be applied to the Whole-Model or as DP-PEFT (Partial). Because the DP-SGD algorithm is foundational, its applicability spans deeply into the hierarchy, yielding DL, FM, LLM, MLLM.
Verbatim from the paper — Data-Centric and Privacy-Preserving Methods
Nearest profiles
FT (full) (0.33), FT (partial) (0.33), PEFT (LoRA, adapters) (0.36), Training (0.37), Adversarial Training (0.38)
Computed from the taxonomy data (Gower distance over all six dimensions)
Cite this row
DP-FT — six-dimensional profile D1: parametric-update D2: privacy-preservation D3: large-labeled, small-labeled D4: ad-hoc-permanent, scheduled-permanent D5: partial, whole-model D6: dl, fm, llm, mllm Source: arXiv:2608.06246Compare with another technique Find in the explorer