A Tail of Two Densities
Published on 2019-03-13

If you only have one minute, here is what differential privacy is about:

Let \(p\) and \(q\) be two probability densities, we define the divergence variable¹ of \((p, q)\) to be

\[L(p || q) := \log {p(\xi) \over q(\xi)}\]

where \(\xi\) is a random variable distributed according to \(p\).

Roughly speaking, differential privacy is the study of the tail bound of \(L(p || q)\): for certain \(p\)s and \(q\)s, and for \(\epsilon > 0\), find \(\delta(\epsilon)\) such that

\[\mathbb P(L(p || q) > \epsilon) < \delta(\epsilon),\]

where \(p\) and \(q\) are the laws of the outputs of a randomised functions on two very similar inputs. Moreover, to make matters even simpler, only three situations need to be considered:

1. (General case) \(q\) is in the form of \(q(y) = p(y + \Delta)\) for some bounded constant \(\Delta\).
2. (Compositions) \(p\) and \(q\) are combinatorial or sequential compositions of some simpler \(p_i\)'s and \(q_i\)'s respectively
3. (Subsampling) \(p\) and \(q\) are mixtures / averages of some simpler \(p_i\)'s and \(q_i\)'s respectively

In applications, the inputs are databases and the randomised functions are queries with an added noise, and the tail bounds give privacy guarantees. When it comes to gradient descent, the input is the training dataset, and the query updates the parameters, and privacy is achieved by adding noise to the gradients.

Now if you have an hour…

Definition (Mechanisms). Let \(X\) be a space with a metric \(d: X \times X \to \mathbb N\). A mechanism \(M\) is a function that takes \(x \in X\) as input and outputs a random variable on \(Y\).

In this post, \(X = Z^m\) is the space of datasets of \(m\) rows for some integer \(m\), where each item resides in some space \(Z\). In this case the distance \(d(x, x') := \#\{i: x_i \neq x'_i\}\) is the number of rows that differ between \(x\) and \(x'\).

Normally we have a query \(f: X \to Y\), and construct the mechanism \(M\) from \(f\) by adding a noise:

\[M(x) := f(x) + \text{noise}.\]

Later, we will also consider mechanisms constructed from composition or mixture of other mechanisms.

In this post \(Y = \mathbb R^d\) for some \(d\).

Definition (Sensitivity). Let \(f: X \to \mathbb R^d\) be a function. The sensitivity \(S_f\) of \(f\) is defined as

\[S_f := \sup_{x, x' \in X: d(x, x') = 1} \|f(x) - f(x')\|_2,\]

where \(\|y\|_2 = \sqrt{y_1^2 + ... + y_d^2}\) is the \(\ell^2\)-norm.

Definition (Differential Privacy). A mechanism \(M\) is called \(\epsilon\)/-differential privacy/ (\(\epsilon\)-dp) if it satisfies the following condition: for all \(x, x' \in X\) with \(d(x, x') = 1\), and for all measureable set \(S \subset \mathbb R^n\),

\[\mathbb P(M(x) \in S) \le e^\epsilon P(M(x') \in S). \qquad (1)\]

Practically speaking, this means given the results from perturbed query on two known databases that differs by one row, it is hard to determine which result is from which database.

An example of \(\epsilon\)-dp mechanism is the Laplace mechanism.

Definition. The Laplace distribution over \(\mathbb R\) with parameter \(b > 0\) has probability density function

\[f_{\text{Lap}(b)}(x) = {1 \over 2 b} e^{- {|x| \over b}}.\]

Definition. Let \(d = 1\). The Laplace mechanism is defined by

\[M(x) = f(x) + \text{Lap}(b).\]

Claim. The Laplace mechanism with

\[b \ge \epsilon^{-1} S_f \qquad (1.5)\]

is \(\epsilon\)-dp.

Proof. Quite straightforward. Let \(p\) and \(q\) be the laws of \(M(x)\) and \(M(x')\) respectively.

\[{p (y) \over q (y)} = {f_{\text{Lap}(b)} (y - f(x)) \over f_{\text{Lap}(b)} (y - f(x'))} = \exp(b^{-1} (|y - f(x')| - |y - f(x)|))\]

Using triangular inequality \(|A| - |B| \le |A - B|\) on the right hand side, we have

\[{p (y) \over q (y)} \le \exp(b^{-1} (|f(x) - f(x')|)) \le \exp(\epsilon)\]

where in the last step we use the condition (1.5). \(\square\)

Unfortunately, \(\epsilon\)-dp does not apply to the most commonly used noise, the Gaussian noise. To fix this, we need to relax the definition a bit.

Definition. A mechanism \(M\) is said to be \((\epsilon, \delta)\)/-differentially private/ if for all \(x, x' \in X\) with \(d(x, x') = 1\) and for all measureable \(S \subset \mathbb R^d\)

\[\mathbb P(M(x) \in S) \le e^\epsilon P(M(x') \in S) + \delta. \qquad (2)\]

Immediately we see that the \((\epsilon, \delta)\)-dp is meaningful only if \(\delta < 1\).

So far we have seen how a mechanism made of a single query plus a noise can be proved to be differentially private. But we need to understand the privacy when composing several mechanisms, combinatorially or sequentially. Let us first define the combinatorial case:

Definition (Independent composition). Let \(M_1, ..., M_k\) be \(k\) mechanisms with independent noises. The mechanism \(M = (M_1, ..., M_k)\) is called the independent composition of \(M_{1 : k}\).

To define the adaptive composition, let us motivate it with an example of gradient descent. Consider the loss function \(\ell(x; \theta)\) of a neural network, where \(\theta\) is the parameter and \(x\) the input, gradient descent updates its parameter \(\theta\) at each time \(t\):

\[\theta_{t} = \theta_{t - 1} - \alpha m^{-1} \sum_{i = 1 : m} \nabla_\theta \ell(x_i; \theta) |_{\theta = \theta_{t - 1}}.\]

We may add privacy by adding noise \(\zeta_t\) at each step:

\[\theta_{t} = \theta_{t - 1} - \alpha m^{-1} \sum_{i = 1 : m} \nabla_\theta \ell(x_i; \theta) |_{\theta = \theta_{t - 1}} + \zeta_t. \qquad (6.97)\]

Viewed as a sequence of mechanism, we have that at each time \(t\), the mechanism \(M_t\) takes input \(x\), and outputs \(\theta_t\). But \(M_t\) also depends on the output of the previous mechanism \(M_{t - 1}\). To this end, we define the adaptive composition.

Definition (Adaptive composition). Let \(({M_i(y_{1 : i - 1})})_{i = 1 : k}\) be \(k\) mechanisms with independent noises, where \(M_1\) has no parameter, \(M_2\) has one parameter in \(Y\), \(M_3\) has two parameters in \(Y\) and so on. For \(x \in X\), define \(\xi_i\) recursively by

\[\begin{aligned} \xi_1 &:= M_1(x)\\ \xi_i &:= M_i(\xi_1, \xi_2, ..., \xi_{i - 1}) (x). \end{aligned}\]

The adaptive composition of \(M_{1 : k}\) is defined by \(M(x) := (\xi_1, \xi_2, ..., \xi_k)\).

The definition of adaptive composition may look a bit complicated, but the point is to describe \(k\) mechanisms such that for each \(i\), the output of the first, second, …, \(i - 1\)th mechanisms determine the \(i\)th mechanism, like in the case of gradient descent.

It is not hard to write down the differentially private gradient descent as a sequential composition:

\[M_t(\theta_{1 : t - 1})(x) = \theta_{t - 1} - \alpha m^{-1} \sum_{i = 1 : m} \nabla_\theta \ell(x_i; \theta) |_{\theta = \theta_{t - 1}} + \zeta_t.\]

In Dwork-Rothblum-Vadhan 2010 (see also Dwork-Roth 2013) the adaptive composition is defined in a more general way, but the definition is based on the same principle, and proofs in this post on adaptive compositions carry over.

It is not hard to see that the adaptive composition degenerates to independent composition when each \(M_i(y_{1 : i})\) evaluates to the same mechanism regardless of \(y_{1 : i}\), in which case the \(\xi_i\)s are independent.

In the following when discussing adaptive compositions we sometimes omit the parameters for convenience without risk of ambiguity, and write \(M_i(y_{1 : i})\) as \(M_i\), but keep in mind of the dependence on the parameters.

It is time to state and prove the composition theorems. In this section we consider \(2 \times 2 \times 2 = 8\) cases, i.e. situations of three dimensions, where there are two choices in each dimension:

1. Composition of \(\epsilon\)-dp or more generally \((\epsilon, \delta)\)-dp mechanisms
2. Composition of independent or more generally adaptive mechanisms
3. Basic or advanced compositions

Note that in the first two dimensions the second choice is more general than the first.

The proofs of these composition theorems will be laid out as follows:

1. Claim 10 - Basic composition theorem for \((\epsilon, \delta)\)-dp with adaptive mechanisms: by a direct proof with an induction argument
2. Claim 14 - Advanced composition theorem for \(\epsilon\)-dp with independent mechanisms: by factorising privacy loss and using Hoeffding's Inequality
3. Claim 16 - Advanced composition theorem for \(\epsilon\)-dp with adaptive mechanisms: by factorising privacy loss and using Azuma's Inequality
4. Claims 17 and 18 - Advanced composition theorem for \((\epsilon, \delta)\)-dp with independent / adaptive mechanisms: by using characterisations of \((\epsilon, \delta)\)-dp in Claims 4 and 5 as an approximation of \(\epsilon\)-dp and then using Proofs in Item 2 or 3.

Claim 10 (Basic composition theorem). Let \(M_{1 : k}\) be \(k\) mechanisms with independent noises such that for each \(i\) and \(y_{1 : i - 1}\), \(M_i(y_{1 : i - 1})\) is \((\epsilon_i, \delta_i)\)-dp. Then the adpative composition of \(M_{1 : k}\) is \((\sum_i \epsilon_i, \sum_i \delta_i)\)-dp.

Proof (Dwork-Lei 2009, see also Dwork-Roth 2013 Appendix B.1). Let \(x\) and \(x'\) be neighbouring points in \(X\). Let \(M\) be the adaptive composition of \(M_{1 : k}\). Define

\[\xi_{1 : k} := M(x), \qquad \eta_{1 : k} := M(x').\]

Let \(p^i\) and \(q^i\) be the laws of \((\xi_{1 : i})\) and \((\eta_{1 : i})\) respectively.

Let \(S_1, ..., S_k \subset Y\) and \(T_i := \prod_{j = 1 : i} S_j\). We use two tricks.

1. Since \(\xi_i | \xi_{< i} = y_{< i}\) and \(\eta_i | \eta_{< i} = y_{< i}\) are \((\epsilon_i, \delta_i)\)-ind, and a probability is no greater than \(1\), \[\begin{aligned} \mathbb P(\xi_i \in S_i | \xi_{< i} = y_{< i}) &\le (e^{\epsilon_i} \mathbb P(\eta_i \in S_i | \eta_{< i} = y_{< i}) + \delta_i) \wedge 1 \\ &\le (e^{\epsilon_i} \mathbb P(\eta_i \in S_i | \eta_{< i} = y_{< i}) + \delta_i) \wedge (1 + \delta_i) \\ &= (e^{\epsilon_i} \mathbb P(\eta_i \in S_i | \eta_{< i} = y_{< i}) \wedge 1) + \delta_i \end{aligned}\]

2. Given \(p\) and \(q\) that are \((\epsilon, \delta)\)-ind, define \[\mu(x) = (p(x) - e^\epsilon q(x))_+.\]

   We have \[\mu(S) \le \delta, \forall S\]

   In the following we define \(\mu^{i - 1} = (p^{i - 1} - e^\epsilon q^{i - 1})_+\) for the same purpose.

We use an inductive argument to prove the theorem:

\[\begin{aligned} \mathbb P(\xi_{\le i} \in T_i) &= \int_{T_{i - 1}} \mathbb P(\xi_i \in S_i | \xi_{< i} = y_{< i}) p^{i - 1} (y_{< i}) dy_{< i} \\ &\le \int_{T_{i - 1}} (e^{\epsilon_i} \mathbb P(\eta_i \in S_i | \eta_{< i} = y_{< i}) \wedge 1) p^{i - 1}(y_{< i}) dy_{< i} + \delta_i\\ &\le \int_{T_{i - 1}} (e^{\epsilon_i} \mathbb P(\eta_i \in S_i | \eta_{< i} = y_{< i}) \wedge 1) (e^{\epsilon_1 + ... + \epsilon_{i - 1}} q^{i - 1}(y_{< i}) + \mu^{i - 1} (y_{< i})) dy_{< i} + \delta_i\\ &\le \int_{T_{i - 1}} e^{\epsilon_i} \mathbb P(\eta_i \in S_i | \eta_{< i} = y_{< i}) e^{\epsilon_1 + ... + \epsilon_{i - 1}} q^{i - 1}(y_{< i}) dy_{< i} + \mu_{i - 1}(T_{i - 1}) + \delta_i\\ &\le e^{\epsilon_1 + ... + \epsilon_i} \mathbb P(\eta_{\le i} \in T_i) + \delta_1 + ... + \delta_{i - 1} + \delta_i.\\ \end{aligned}\]

In the second line we use Trick 1; in the third line we use the induction assumption; in the fourth line we multiply the first term in the first braket with first term in the second braket, and the second term (i.e. \(1\)) in the first braket with the second term in the second braket (i.e. the \(\mu\) term); in the last line we use Trick 2.

The base case \(i = 1\) is true since \(M_1\) is \((\epsilon_1, \delta_1)\)-dp. \(\square\)

To prove the advanced composition theorem, we start with some lemmas.

Claim 11. If \(p\) and \(q\) are \(\epsilon\)-ind, then

\[D(p || q) + D(q || p) \le \epsilon(e^\epsilon - 1).\]

Proof. Since \(p\) and \(q\) are \(\epsilon\)-ind, we have \(|\log p(x) - \log q(x)| \le \epsilon\) for all \(x\). Let \(S := \{x: p(x) > q(x)\}\). Then we have on

\[\begin{aligned} D(p || q) + D(q || p) &= \int (p(x) - q(x)) (\log p(x) - \log q(x)) dx\\ &= \int_S (p(x) - q(x)) (\log p(x) - \log q(x)) dx + \int_{S^c} (q(x) - p(x)) (\log q(x) - \log p(x)) dx\\ &\le \epsilon(\int_S p(x) - q(x) dx + \int_{S^c} q(x) - p(x) dx) \end{aligned}\]

Since on \(S\) we have \(q(x) \le p(x) \le e^\epsilon q(x)\), and on \(S^c\) we have \(p(x) \le q(x) \le e^\epsilon p(x)\), we obtain

\[D(p || q) + D(q || p) \le \epsilon \int_S (1 - e^{-\epsilon}) p(x) dx + \epsilon \int_{S^c} (e^{\epsilon} - 1) p(x) dx \le \epsilon (e^{\epsilon} - 1),\]

where in the last step we use \(e^\epsilon - 1 \ge 1 - e^{- \epsilon}\) and \(p(S) + p(S^c) = 1\). \(\square\)

Claim 12. If \(p\) and \(q\) are \(\epsilon\)-ind, then

\[D(p || q) \le a(\epsilon) \ge D(q || p),\]

where

\[a(\epsilon) = \epsilon (e^\epsilon - 1) 1_{\epsilon \le \log 2} + \epsilon 1_{\epsilon > \log 2} \le (\log 2)^{-1} \epsilon^2 1_{\epsilon \le \log 2} + \epsilon 1_{\epsilon > \log 2}. \qquad (6.98)\]

Proof. Since \(p\) and \(q\) are \(\epsilon\)-ind, we have

\[D(p || q) = \mathbb E_{\xi \sim p} \log {p(\xi) \over q(\xi)} \le \max_y {\log p(y) \over \log q(y)} \le \epsilon.\]

Comparing the quantity in Claim 11 (\(\epsilon(e^\epsilon - 1)\)) with the quantity above (\(\epsilon\)), we arrive at the conclusion. \(\square\)

Claim 13 (Hoeffding's Inequality). Let \(L_i\) be independent random variables with \(|L_i| \le b\), and let \(L = L_1 + ... + L_k\), then for \(t > 0\),

\[\mathbb P(L - \mathbb E L \ge t) \le \exp(- {t^2 \over 2 k b^2}).\]

Claim 14 (Advanced Independent Composition Theorem) (\(\delta = 0\)). Fix \(0 < \beta < 1\). Let \(M_1, ..., M_k\) be \(\epsilon\)-dp, then the independent composition \(M\) of \(M_{1 : k}\) is \((k a(\epsilon) + \sqrt{2 k \log \beta^{-1}} \epsilon, \beta)\)-dp.

Remark. By (6.98) we know that \(k a(\epsilon) + \sqrt{2 k \log \beta^{-1}} \epsilon = \sqrt{2 k \log \beta^{-1}} \epsilon + k O(\epsilon^2)\) when \(\epsilon\) is sufficiently small, in which case the leading term is of order \(O(\sqrt k \epsilon)\) and we save a \(\sqrt k\) in the \(\epsilon\)-part compared to the Basic Composition Theorem (Claim 10).

Remark. In practice one can try different choices of \(\beta\) and settle with the one that gives the best privacy guarantee. See the discussions at the end of Part 2 of this post.

Proof. Let \(p_i\), \(q_i\), \(p\) and \(q\) be the laws of \(M_i(x)\), \(M_i(x')\), \(M(x)\) and \(M(x')\) respectively.

\[\mathbb E L_i = D(p_i || q_i) \le a(\epsilon),\]

where \(L_i := L(p_i || q_i)\). Due to \(\epsilon\)-ind also have

\[|L_i| \le \epsilon.\]

Therefore, by Hoeffding's Inequality,

\[\mathbb P(L - k a(\epsilon) \ge t) \le \mathbb P(L - \mathbb E L \ge t) \le \exp(- t^2 / 2 k \epsilon^2),\]

where \(L := \sum_i L_i = L(p || q)\).

Plugging in \(t = \sqrt{2 k \epsilon^2 \log \beta^{-1}}\), we have

\[\mathbb P(L(p || q) \le k a(\epsilon) + \sqrt{2 k \epsilon^2 \log \beta^{-1}}) \ge 1 - \beta.\]

Similarly we also have

\[\mathbb P(L(q || p) \le k a(\epsilon) + \sqrt{2 k \epsilon^2 \log \beta^{-1}}) \ge 1 - \beta.\]

By Claim 1 we arrive at the conclusion. \(\square\)

Claim 15 (Azuma's Inequality). Let \(X_{0 : k}\) be a supermartingale. If \(|X_i - X_{i - 1}| \le b\), then

\[\mathbb P(X_k - X_0 \ge t) \le \exp(- {t^2 \over 2 k b^2}).\]

Azuma's Inequality implies a slightly weaker version of Hoeffding's Inequality. To see this, let \(L_{1 : k}\) be independent variables with \(|L_i| \le b\). Let \(X_i = \sum_{j = 1 : i} L_j - \mathbb E L_j\). Then \(X_{0 : k}\) is a martingale, and

\[| X_i - X_{i - 1} | = | L_i - \mathbb E L_i | \le 2 b,\]

since \(\|L_i\|_1 \le \|L_i\|_\infty\). Hence by Azuma's Inequality,

\[\mathbb P(L - \mathbb E L \ge t) \le \exp(- {t^2 \over 8 k b^2}).\]

Of course here we have made no assumption on \(\mathbb E L_i\). If instead we have some bound for the expectation, say \(|\mathbb E L_i| \le a\), then by the same derivation we have

\[\mathbb P(L - \mathbb E L \ge t) \le \exp(- {t^2 \over 2 k (a + b)^2}).\]

It is not hard to see what Azuma is to Hoeffding is like adaptive composition to independent composition. Indeed, we can use Azuma's Inequality to prove the Advanced Adaptive Composition Theorem for \(\delta = 0\).

Claim 16 (Advanced Adaptive Composition Theorem) (\(\delta = 0\)). Let \(\beta > 0\). Let \(M_{1 : k}\) be \(k\) mechanisms with independent noises such that for each \(i\) and \(y_{1 : i}\), \(M_i(y_{1 : i})\) is \((\epsilon, 0)\)-dp. Then the adpative composition of \(M_{1 : k}\) is \((k a(\epsilon) + \sqrt{2 k \log \beta^{-1}} (\epsilon + a(\epsilon)), \beta)\)-dp.

Proof. As before, let \(\xi_{1 : k} \overset{d}{=} M(x)\) and \(\eta_{1 : k} \overset{d}{=} M(x')\), where \(M\) is the adaptive composition of \(M_{1 : k}\). Let \(p_i\) (resp. \(q_i\)) be the law of \(\xi_i | \xi_{< i}\) (resp. \(\eta_i | \eta_{< i}\)). Let \(p^i\) (resp. \(q^i\)) be the law of \(\xi_{\le i}\) (resp. \(\eta_{\le i}\)). We want to construct supermartingale \(X\). To this end, let

\[X_i = \log {p^i(\xi_{\le i}) \over q^i(\xi_{\le i})} - i a(\epsilon) \]

We show that \((X_i)\) is a supermartingale:

\[\begin{aligned} \mathbb E(X_i - X_{i - 1} | X_{i - 1}) &= \mathbb E \left(\log {p_i (\xi_i | \xi_{< i}) \over q_i (\xi_i | \xi_{< i})} - a(\epsilon) | \log {p^{i - 1} (\xi_{< i}) \over q^{i - 1} (\xi_{< i})}\right) \\ &= \mathbb E \left( \mathbb E \left(\log {p_i (\xi_i | \xi_{< i}) \over q_i (\xi_i | \xi_{< i})} | \xi_{< i}\right) | \log {p^{i - 1} (\xi_{< i}) \over q^{i - 1} (\xi_{< i})}\right) - a(\epsilon) \\ &= \mathbb E \left( D(p_i (\cdot | \xi_{< i}) || q_i (\cdot | \xi_{< i})) | \log {p^{i - 1} (\xi_{< i}) \over q^{i - 1} (\xi_{< i})}\right) - a(\epsilon) \\ &\le 0, \end{aligned}\]

since by Claim 12 \(D(p_i(\cdot | y_{< i}) || q_i(\cdot | y_{< i})) \le a(\epsilon)\) for all \(y_{< i}\).

Since

\[| X_i - X_{i - 1} | = | \log {p_i(\xi_i | \xi_{< i}) \over q_i(\xi_i | \xi_{< i})} - a(\epsilon) | \le \epsilon + a(\epsilon),\]

by Azuma's Inequality,

\[\mathbb P(\log {p^k(\xi_{1 : k}) \over q^k(\xi_{1 : k})} \ge k a(\epsilon) + t) \le \exp(- {t^2 \over 2 k (\epsilon + a(\epsilon))^2}). \qquad(6.99)\]

Let \(t = \sqrt{2 k \log \beta^{-1}} (\epsilon + a(\epsilon))\) we are done. \(\square\)

Claim 17 (Advanced Independent Composition Theorem). Fix \(0 < \beta < 1\). Let \(M_1, ..., M_k\) be \((\epsilon, \delta)\)-dp, then the independent composition \(M\) of \(M_{1 : k}\) is \((k a(\epsilon) + \sqrt{2 k \log \beta^{-1}} \epsilon, k \delta + \beta)\)-dp.

Proof. By Claim 4, there exist events \(E_{1 : k}\) and \(F_{1 : k}\) such that

1. The laws \(p_{i | E_i}\) and \(q_{i | F_i}\) are \(\epsilon\)-ind.
2. \(\mathbb P(E_i), \mathbb P(F_i) \ge 1 - \delta\).

Let \(E := \bigcap E_i\) and \(F := \bigcap F_i\), then they both have probability at least \(1 - k \delta\), and \(p_{i | E}\) and \(q_{i | F}\) are \(\epsilon\)-ind.

By Claim 14, \(p_{|E}\) and \(q_{|F}\) are \((\epsilon' := k a(\epsilon) + \sqrt{2 k \epsilon^2 \log \beta^{-1}}, \beta)\)-ind. Let us shrink the bigger event between \(E\) and \(F\) so that they have equal probabilities. Then

\[\begin{aligned} p (S) &\le p_{|E}(S) \mathbb P(E) + \mathbb P(E^c) \\ &\le (e^{\epsilon'} q_{|F}(S) + \beta) \mathbb P(F) + k \delta\\ &\le e^{\epsilon'} q(S) + \beta + k \delta. \end{aligned}\]

\(\square\)

Claim 18 (Advanced Adaptive Composition Theorem). Fix \(0 < \beta < 1\). Let \(M_{1 : k}\) be \(k\) mechanisms with independent noises such that for each \(i\) and \(y_{1 : i}\), \(M_i(y_{1 : i})\) is \((\epsilon, \delta)\)-dp. Then the adpative composition of \(M_{1 : k}\) is \((k a(\epsilon) + \sqrt{2 k \log \beta^{-1}} (\epsilon + a(\epsilon)), \beta + k \delta)\)-dp.

Remark. This theorem appeared in Dwork-Rothblum-Vadhan 2010, but I could not find a proof there. A proof can be found in Dwork-Roth 2013 (See Theorem 3.20 there). Here I prove it in a similar way, except that instead of the use of an intermediate random variable there, I use the conditional probability results from Claim 5, the approach mentioned in Vadhan 2017.

Proof. By Claim 5, there exist events \(E_{1 : k}\) and \(F_{1 : k}\) such that

1. The laws \(p_{i | E_i}(\cdot | y_{< i})\) and \(q_{i | F_i}(\cdot | y_{< i})\) are \(\epsilon\)-ind for all \(y_{< i}\).
2. \(\mathbb P(E_i | y_{< i}), \mathbb P(F_i | y_{< i}) \ge 1 - \delta\) for all \(y_{< i}\).

Let \(E := \bigcap E_i\) and \(F := \bigcap F_i\), then they both have probability at least \(1 - k \delta\), and \(p_{i | E}(\cdot | y_{< i}\) and \(q_{i | F}(\cdot | y_{< i})\) are \(\epsilon\)-ind.

By Advanced Adaptive Composition Theorem (\(\delta = 0\)), \(p_{|E}\) and \(q_{|F}\) are \((\epsilon' := k a(\epsilon) + \sqrt{2 k \log \beta^{-1}} (\epsilon + a(\epsilon)), \beta)\)-ind.

The rest is the same as in the proof of Claim 17. \(\square\)

Stochastic gradient descent is like gradient descent, but with random subsampling.

Recall we have been considering databases in the space \(Z^m\). Let \(n < m\) be a positive integer, \(\mathcal I := \{I \subset [m]: |I| = n\}\) be the set of subsets of \([m]\) of size \(n\), and \(\gamma\) a random subset sampled uniformly from \(\mathcal I\). Let \(r = {n \over m}\) which we call the subsampling rate. Then we may add a subsampling module to the noisy gradient descent algorithm (6.97) considered before

\[\theta_{t} = \theta_{t - 1} - \alpha n^{-1} \sum_{i \in \gamma} \nabla_\theta h_\theta(x_i) |_{\theta = \theta_{t - 1}} + \zeta_t. \qquad (7)\]

It turns out subsampling has an amplification effect on privacy.

Claim 19 (Ullman 2017). Fix \(r \in [0, 1]\). Let \(n \le m\) be two nonnegative integers with \(n = r m\). Let \(N\) be an \((\epsilon, \delta)\)-dp mechanism on \(Z^n\). Define mechanism \(M\) on \(Z^m\) by

\[M(x) = N(x_\gamma)\]

Then \(M\) is \((\log (1 + r(e^\epsilon - 1)), r \delta)\)-dp.

Remark. Some seem to cite Kasiviswanathan-Lee-Nissim-Raskhodnikova-Smith 2005 for this result, but it is not clear to me how it appears there.

Proof. Let \(x, x' \in Z^n\) such that they differ by one row \(x_i \neq x_i'\). Naturally we would like to consider the cases where the index \(i\) is picked and the ones where it is not separately. Let \(\mathcal I_\in\) and \(\mathcal I_\notin\) be these two cases:

\[\begin{aligned} \mathcal I_\in = \{J \subset \mathcal I: i \in J\}\\ \mathcal I_\notin = \{J \subset \mathcal I: i \notin J\}\\ \end{aligned}\]

We will use these notations later. Let \(A\) be the event \(\{\gamma \ni i\}\).

Let \(p\) and \(q\) be the laws of \(M(x)\) and \(M(x')\) respectively. We collect some useful facts about them. First due to \(N\) being \((\epsilon, \delta)\)-dp,

\[p_{|A}(S) \le e^\epsilon q_{|A}(S) + \delta.\]

Also,

\[p_{|A}(S) \le e^\epsilon p_{|A^c}(S) + \delta.\]

To see this, note that being conditional laws, \(p_A\) and \(p_{A^c}\) are averages of laws over \(\mathcal I_\in\) and \(\mathcal I_\notin\) respectively:

\[\begin{aligned} p_{|A}(S) = |\mathcal I_\in|^{-1} \sum_{I \in \mathcal I_\in} \mathbb P(N(x_I) \in S)\\ p_{|A^c}(S) = |\mathcal I_\notin|^{-1} \sum_{J \in \mathcal I_\notin} \mathbb P(N(x_J) \in S). \end{aligned}\]

Now we want to pair the \(I\)'s in \(\mathcal I_\in\) and \(J\)'s in \(\mathcal I_\notin\) so that they differ by one index only, which means \(d(x_I, x_J) = 1\). Formally, this means we want to consider the set:

\[\mathcal D := \{(I, J) \in \mathcal I_\in \times \mathcal I_\notin: |I \cap J| = n - 1\}.\]

We may observe by trying out some simple cases that every \(I \in \mathcal I_\in\) is paired with \(n\) elements in \(\mathcal I_\notin\), and every \(J \in \mathcal I_\notin\) is paired with \(m - n\) elements in \(\mathcal I_\in\). Therefore

\[p_{|A}(S) = |\mathcal D|^{-1} \sum_{(I, J) \in \mathcal D} \mathbb P(N(x_I \in S)) \le |\mathcal D|^{-1} \sum_{(I, J) \in \mathcal D} (e^\epsilon \mathbb P(N(x_J \in S)) + \delta) = e^\epsilon p_{|A^c} (S) + \delta.\]

Since each of the \(m\) indices is picked independently with probability \(r\), we have

\[\mathbb P(A) = r.\]

Let \(t \in [0, 1]\) to be determined. We may write

\[\begin{aligned} p(S) &= r p_{|A} (S) + (1 - r) p_{|A^c} (S)\\ &\le r(t e^\epsilon q_{|A}(S) + (1 - t) e^\epsilon q_{|A^c}(S) + \delta) + (1 - r) q_{|A^c} (S)\\ &= rte^\epsilon q_{|A}(S) + (r(1 - t) e^\epsilon + (1 - r)) q_{|A^c} (S) + r \delta\\ &= te^\epsilon r q_{|A}(S) + \left({r \over 1 - r}(1 - t) e^\epsilon + 1\right) (1 - r) q_{|A^c} (S) + r \delta \\ &\le \left(t e^\epsilon \wedge \left({r \over 1 - r} (1 - t) e^\epsilon + 1\right)\right) q(S) + r \delta. \qquad (7.5) \end{aligned}\]

We can see from the last line that the best bound we can get is when

\[t e^\epsilon = {r \over 1 - r} (1 - t) e^\epsilon + 1.\]

Solving this equation we obtain

\[t = r + e^{- \epsilon} - r e^{- \epsilon}\]

and plugging this in (7.5) we have

\[p(S) \le (1 + r(e^\epsilon - 1)) q(S) + r \delta.\]

\(\square\)

Since \(\log (1 + x) < x\) for \(x > 0\), we can rewrite the conclusion of the Claim to \((r(e^\epsilon - 1), r \delta)\)-dp. Further more, if \(\epsilon < \alpha\) for some \(\alpha\), we can rewrite it as \((r \alpha^{-1} (e^\alpha - 1) \epsilon, r \delta)\)-dp or \((O(r \epsilon), r \delta)\)-dp.

Let \(\epsilon < 1\). We see that if the mechanism \(N\) is \((\epsilon, \delta)\)-dp on \(Z^n\), then \(M\) is \((2 r \epsilon, r \delta)\)-dp, and if we run it over \(k / r\) minibatches, by Advanced Adaptive Composition theorem, we have \((\sqrt{2 k r \log \beta^{-1}} \epsilon + 2 k r \epsilon^2, k \delta + \beta)\)-dp.

This is better than the privacy guarantee without subsampling, where we run over \(k\) iterations and obtain \((\sqrt{2 k \log \beta^{-1}} \epsilon + 2 k \epsilon^2, k \delta + \beta)\)-dp. So with subsampling we gain an extra \(\sqrt r\) in the \(\epsilon\)-part of the privacy guarantee. But, smaller subsampling rate means smaller minibatch size, which would result in bigger variance, so there is a trade-off here.

Finally we define the differentially private stochastic gradient descent (DP-SGD) with the Gaussian mechanism (Abadi-Chu-Goodfellow-McMahan-Mironov-Talwar-Zhang 2016), which is (7) with the noise specialised to Gaussian and an added clipping operation to bound to sensitivity of the query to a chosen \(C\):

\[\theta_{t} = \theta_{t - 1} - \alpha \left(n^{-1} \sum_{i \in \gamma} \nabla_\theta \ell(x_i; \theta) |_{\theta = \theta_{t - 1}}\right)_{\text{Clipped at }C / 2} + N(0, \sigma^2 C^2 I),\]

where

\[y_{\text{Clipped at } \alpha} := y / (1 \vee {\|y\|_2 \over \alpha})\]

is \(y\) clipped to have norm at most \(\alpha\).

Note that the clipping in DP-SGD is much stronger than making the query have sensitivity \(C\). It makes the difference between the query results of two arbitrary inputs bounded by \(C\), rather than neighbouring inputs.

In Part 2 of this post we will use the tools developed above to discuss the privacy guarantee for DP-SGD, among other things.

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