v152 · privacy · demo

Histogram Explorer

Private Aggregation added Laplace noise to every histogram bucket before reporting. This explorer lets you see what that noise actually looked like: pick a bucket key and true value, set epsilon, and watch how the noisy distribution shifts. Understanding the mechanism helps clarify why removal simplifies the stack.

Private Aggregation is removed in Chrome 152. The histogram mechanism below is educational — it simulates what the API did internally. Your replacement is direct first-party reporting with server-side aggregation and no in-browser DP step.

sharedStorage (worklet host): checking…

bucket key

The histogram dimension. e.g. "ad placement = 1024"

true value

500

The contribution you want to report (e.g. conversion count × 100).

privacy budget ε

1.0

Lower ε = more noise = more privacy. Higher ε = less noise = more utility.

single noisy value

One report as the API would have sent it:

500

true value ↓

—

noisy reported value

signal / noise ratio

metricvalue
true value—
expected noise std dev—
last noisy value—
error this sample—
relative error—
utility rating—

Utility:

distribution of 200 simulated noisy reports (click "Simulate 200 reports"):

x-axis: reported value  |  y-axis: count  |  red line: true value

what the API did

// Inside a Shared Storage worklet (now removed):
class MyOperation {
  async run(data) {
    // bucket = which histogram dimension (0–2^128)
    // value  = contribution (0–L1 budget, default 65536)
    privateAggregation.contributeToHistogram({
      bucket: BigInt(data.bucketKey),
      value:  data.value
    });
    // Browser adds Laplace noise:
    //   scale  = sensitivity / epsilon  (sensitivity = 1 by default)
    //   noisy  = value + Laplace(0, scale)
    // Then aggregates across users before releasing the report.
  }
}
// Registered as: sharedStorage.run("my-op", { data: {...} })

The noise scale is sensitivity / epsilon. With epsilon = 1 and the default sensitivity of 1, the standard deviation is √2 ≈ 1.41. For a value of 500 that’s negligible. For a value of 5 it’s huge. That’s why low-count measurements were the hardest use case.

see also