I stumbled upon a paper that proposes local DP around this argument:

  • A user $u_i$ generates a sequence $s_{i}$ of observations at certain timestamps:

$$ s = ((t_1, x_1), (t_2, x_2), \dots, (t_n, x_n)) $$

  • The authors apply $(\varepsilon/n, 0)$-DP to each sequence by adding Laplacian noise
  • As widely known, Laplacian must be of scale $b = \frac{\Delta f}{ \text{budget}}$

The authors propose budget of $\varepsilon / n$, which is IMO correct. But they also define $\Delta f$, aka the sensitivity of the query, as simply the range of any value at any timestamp, $\text{max}(x) - \text{min}(x)$.

I'm not convinced that this is the true sensitivity. To my understanding, the query output is (ignoring the timestamps) not a single value $\mathbb{R}$ but rather the vector of outputs $\mathbb{R}^n$, so per definition of sensitivity $\ell_1$-sensitivity of a function $f : \mathbb{N}^{|\mathcal{X}|} \rightarrow \mathbb{R}^k$:

$$ \Delta f = \max_{x, y \in \mathbb{N}; \| x - y\|_1 = 1} \| f(x) - f(y) \|_1 $$

and properly computing the $\ell_1$ norm as $\| x - y\|_1 = \sum_{i = 1}^{k} | x_i - y_i |$, the sensitivity should be

$$(\text{max}(x) - \text{min}(x))^n$$

Is my reasoning correct (and the paper's DP potentially wrong), or am I missing something? (I don't reveal the paper on purpose.)

Update: Claryfing context of the time series.

The health data stream each user is represented as a sequence $s = ((t_1, x_1), (t_2, x_2), \dots, (t_n, x_n))$

Here, $(t_d, x_d)$ represents the $d$-th point in the stream where $x_d$ denotes the value measured by the wearable health device at timestamp $t_d$.

We further assume that $x_d$, which is measured by the specific sensor in a wearable health device, is within the predefined range $[x_{min}, x_{max}]$.

Their particular use-case is collecting heart rate ($x$) over time ($t$).


2 Answers 2


I think you should explain what is $x_i$ in this time series. I would recommend linking the paper. I think without a strong context we could answer wrongly because maybe you could have understood something wrong as it has happened to me sometimes. Some points:

  1. Query output could be $\mathbb{R}$ or $\mathbb{R}^n$, depends what kind of query you are defining and using.

  2. You are right, I suspect they are defining a global sensitivity because maybe is easier to compute. But this depends on the kind of query they are defining on the paper, is a linear query, an only-one query or a non-linear query

  • $\begingroup$ Thanks, I've updated the question, see above. Here's the paper's DOI: 10.1371/journal.pone.0207639 $\endgroup$
    – John Doe
    Commented Jan 25, 2021 at 12:38

I don't like answering my questions but it seems I misunderstood it and the authors are indeed correct.

Our query wants to obtain $N$ measurements from the database (database of size one but that's not relevant) with overall sensitivity budget $\varepsilon$. We can also split the single query into $N$ independent queries (more on the dependency below), where each query simply asks "What is the value at point $n$". Each of these $N$ queries has much smaller budget $\frac{\varepsilon}{N}$ which satisfies, by query composition, the overall budget $\varepsilon$.

Now each "small" $n$-th query has to satisfy $(\frac{\varepsilon}{N}, 0)$-DP, for which we need a mechanism and the sensitivity of this "small" query. In our case, the sensitivity is the range of values at point $n$, and the mechanism is Laplace as usual.

Regarding dependency between points $x_n$ and $x_{n+1}$. If we knew they are dependent, for example $x_{n+1}$ is simply $2 \cdot {x_n}$, we would only need to allocate the privacy budget $\frac{2 \varepsilon}{N}$ to $x_n$ and $0$ to $x_{n + 1}$. So the mechanism needs to randomize only $x_n$. But we pretend to have no knowledge whatsoever, i.e. the dependency of queries $n$ and $n + 1$, and expect the worst case by protecting each query independently. So even if $x_n$ and $x_{n+1}$ are 100% correlated, we add noise to each of them independently.

(I think a helpful visualization are two points in $\mathbb{R}^2$ and adding multivariate gaussian to each of them; correlated and uncorrelated, and look at the bounds of these two distribution in any point from the possible range)


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