Pith Number

pith:PW2WXAGK

pith:2026:PW2WXAGKN3HPRXCOBDKE5EJ5B2

not attested not anchored not stored refs pending

Efficient Propose-Test-Release for Optimal Differentially Private Estimation

Tao Shen, Wanjie Wang, Xin T. Tong

Efficient PTR replaces the exact insensitive set with a simpler subset and the exact Hellinger distance with a Lipschitz lower bound, yielding tractable DP mechanisms that attain minimax rates for classification and regression.

arxiv:2605.03264 v2 · 2026-05-05 · stat.ME

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{PW2WXAGKN3HPRXCOBDKE5EJ5B2}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

We introduce efficient PTR (ePTR), which replaces the exact insensitive set with a simpler subset and the exact Hellinger distance with a Lipschitz-based lower bound. This flexibility enables substantially simpler DP mechanisms that achieve rate-optimal accuracy in many settings.

C2weakest assumption

That the chosen simpler insensitive subset and the Lipschitz lower bound on Hellinger distance are tight enough to preserve both the differential privacy guarantee and the claimed minimax optimality for the three concrete estimators studied.

C3one line summary

Efficient PTR replaces exact insensitive sets and Hellinger distances with simpler subsets and Lipschitz lower bounds to achieve minimax-optimal accuracy for DP Bayes classification, linear regression, and nonparametric regression.

Cited by

1 paper in Pith

NetPTR: Optimal Differentially Private Spectral Community Detection on Sparse Networks

Receipt and verification

First computed	2026-07-02T00:18:29.443744Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

7db56b80ca6ecef8dc4e08d44e913d0e9eb15e0cf44d9b331bf6905a376cd0d2

Aliases

arxiv: 2605.03264 · arxiv_version: 2605.03264v2 · doi: 10.48550/arxiv.2605.03264 · pith_short_12: PW2WXAGKN3HP · pith_short_16: PW2WXAGKN3HPRXCO · pith_short_8: PW2WXAGK

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/PW2WXAGKN3HPRXCOBDKE5EJ5B2 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 7db56b80ca6ecef8dc4e08d44e913d0e9eb15e0cf44d9b331bf6905a376cd0d2

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "99891808142a17667271bd46488eca34b5f67bc06717a7a9f62fe9913cddac8f",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2026-05-05T01:31:56Z",
    "title_canon_sha256": "56a1c325f62746359c4e21accee691ca12889c71cb6b57d15781c9bb0d585d9e"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.03264",
    "kind": "arxiv",
    "version": 2
  }
}