Instance-optimal Mean Estimation Under Differential Privacy
Year: 2021

Abstract:
Mean estimation under differential privacy is a fundamental problem, but worstcase optimal mechanisms do not offer meaningful utility guarantees in practice when the global sensitivity is very large. Instead, various heuristics have been proposed to reduce the error on real-world data that do not resemble the worst-case instance. This paper takes a principled approach, yielding a mechanism that is instance-optimal in a strong sense. In addition to its theoretical optimality, the mechanism is also simple and practical, and adapts to a variety of data characteristics without the need of parameter tuning. It easily extends to the local and shuffle model as well.

heading: 1 Introduction
text: Mean estimation is one of the most fundamental problems in statistics, optimization, and machine learning. However, privacy concerns forbid us from using the exact mean in these applications, and the problem of how to achieve the smallest error under a given privacy model has received considerable attention in the literature. Differential privacy (DP) is a rigorous mathematical definition for protecting individual privacy and has emerged as the golden standard in privacy-preserving data analysis nowadays, which has been deployed by Apple [16], Google [22], and Microsoft [17].
Given a data set D := {xi}i∈[n] ⊂ Ud, where U = [u], i.e., each coordinate of the input vector is an integer (real-valued coordinates can be handled by quantization; see remark 1), our goal is to obtain a differentially private estimation M(D) for the mean f(D) = 1n ∑n i=1 xi with small `2
error ‖M(D) − f(D)‖2. Because f(·) has global `2 sensitivity GSf = √ du/n, the standard DP mechanism just adds Gaussian noise scaled to GSf to each coordinate of f(D), which results in an `2 error proportional to du/n. This simple mechanism is worst-case optimal [28], but it is certainly undesirable in practice, as people often conservatively use a large u (e.g., u = 232) but the actual dataset D may have much smaller coordinates. Instead, the clipped-mean estimator [1] (see Section 3.1 for details) has been widely used as an effective heuristic, but two questions remain unresolved: (1) how to choose the clipping threshold C; and (2) if it can yield any optimality guarantees. We answer these questions in a fairly strong sense in this paper.

heading: 1.1 Instance Optimality
text: As worst-case optimality is theoretically trivial and practically meaningless for the mean estimation problem when the global sensitivity is too large, one may aim at instance optimality. More precisely, letM be the class of DP mechanisms and let
Rins(D) := inf M ′∈M inf{ξ | Pr[‖M ′(D)− f(D)‖2 ≤ ξ] ≥ 2/3}
35th Conference on Neural Information Processing Systems (NeurIPS 2021).
be the smallest error anyM ′ can achieve (with constant probability) onD, then the standard definition of instance optimality requires us to design an M such that
Pr[‖M(D)− f(D)‖2 ≤ c · Rins(D)] ≥ 2/3 (1)
for every D, where c is called the optimality ratio. Unfortunately, for any D, one can design a trivial M ′(·) ≡ f(D) that has 0 error on D (but fails miserably on other instances), soRins(·) ≡ 0, which rules out instance-optimal DP mechanisms by a standard argument [21].
SinceRins(·) is unachievable, relaxed versions can be considered. The above trivialM ′ exists because it is only required to work well on one instance D. Imposing higher requirements on M ′ would yield relaxed notions of instance optimality. One natural requirement is that M ′ should work well not just on D, but also on its neighbors, i.e., we raise the target error fromRins(D) to
Rnbr(D) := inf M ′∈M sup D′:dham(D,D′)≤1 inf{ξ | Pr[‖M ′(D′)− f(D′)‖2 ≤ ξ] ≥ 2/3}.
Vahdan [35] observes thatRnbr(D) is exactly LSf (D), the local sensitivity of f at D, up to constant factors. However, LSf (·) may not be an appropriate target to shoot at, depending on what f is. For the MEDIAN problem, LSf (D) = 0 for certain D’s and no DP mechanisms can achieve this error [33], while for mean estimation, LSf (D) = Θ(GSf ) = Θ( √ du/n) for all D, so this relaxation turns instance optimality into worst-case optimality.
The reason why the above relaxation is “too much” for the mean estimation problem is that D′ may change one vector of D arbitrarily, e.g., from (0, . . . , 0) to (u, . . . , u). We restrict this. More precisely, letting supp(D) denote the set of distinct vectors in D, we consider the target error
Rin-nbr(D) := inf M ′∈M sup D′:dham(D,D′)≤1,supp(D′)⊆supp(D) inf{ξ | Pr[‖M ′(D′)−f(D′)‖2 ≤ ξ] ≥ 2/3},
namely, we require M ′ to work well only on D and its in-neighbors, in which a vector can only be changed to another one already existing in D. Correspondingly, an instance-optimal M (w.r.t. the in-neighborhood) is one such that (1) holds whereRins is replaced byRin-nbr. We make a few notes on this notion of instance optimality: (1) This optimality is only about the utility of the mechanism, not its privacy. We still require the mechanism to satisfy the DP requirement between any D,D′ such that dham(D,D′) = 1, not necessarily one and its in-neighbors. (2) In general, a smaller neighborhood leads to a stronger notion of instance optimality. Thus, the optimality using in-neighbors is stronger than that using all neighbors, which is in turn stronger than worst-case optimality (i.e., D′ can be any instance), while the latter two are actually the same for the mean estimation problem. (3) For an instance-optimal M (by our notion), there still exist D,M ′ such that M ′ does better on D than M , but it is not possible for M ′ to achieve a smaller error than the error of M on D over all in-neighbor of D. This is more meaningful than ranging over all neighbors of D, some of which (e.g., one with (u, . . . , u) as a datum) are unlikely to be the actual instances encountered in practice.

heading: 1.2 Our Results
text: To design an M(D) for the mean function f(D) = 1n ∑n i=1 xi that achieves an error w.r.t.Rin-nbr(D) for allD, we need an upper bound and a lower bound. For the lower bound, we show thatRin-nbr(D) = Ω(w(D)/n), where w(D) := max1≤i<j≤n ‖xi − xj‖2 is the diameter of D. Thus, from the upper bound side, it suffices to show that the mechanism’s error is bounded by c ·w(D)/n. This is achieved in two steps. First, we use the clipped-mean estimator, but find the clipping threshold C that optimizes its bias-variance trade-off, which is a certain quantile of the norms of the vectors in D. However, we cannot use the optimal C directly, as it would violate DP. Thus, we use a simple binary search based algorithm that can find any specific quantile privately with an optimal rank error. This results in a DP mechanism with error Õ( √ d/ρ) ·r(D)/n, where r(D) := maxi ‖xi‖2 and ρ is the privacy parameter (formal definition given in Section 2). To reduce the error from r(D) to w(D), in the second step, we rotate and shift D into a D̃ such that r(D̃) = O(w(D)) w.h.p., and apply the clipped-mean estimator (with our privatized optimal clipping threshold) on D̃, leading to an error of Õ( √ d/ρ) · w(D)/n for
n = Ω̃( √ d/ρ). We also show that the optimality ratio c = Õ( √ d/ρ) is optimal, i.e., any mechanism
M(D) having error c · w(D)/n for all D must have c = Ω̃( √ d/ρ) for ρ < Õ( √ d/n).
Our mechanism has the following applications: (1) It can be applied directly to statistical mean estimation, where the vectors in D are i.i.d. samples from a certain distribution and one would like to estimate the mean of the distribution (in contrast, the version defined above is referred to as empirical mean estimation). For concreteness, we show how this is done for the multivariate Gaussian distributions N (µ,Σ). For the case Σ = I, our algorithm achieves an `2 error of α using n = Õ( dα2 + d α √ ρ ) samples for α ≤ O(1), matching the optimal bound in the statistical setting [8]. For a non-identity, unknown Σ, the error is proportional to ‖Σ1/2‖2 as in [8]. Our mechanism requires only crude a priori bounds on µ and Σ (i.e., the error depends on these bounds logarithmically), while [8] needs a constant-factor approximation of Σ, which can be obtained using n = Ω̃(d3/2/ √ ρ) samples [26]. Note that this can be a √ d-factor higher than the sample complexity of mean estimation. Fundamentally, estimating Σ is harder than estimating µ, and we bypass the former so as to retain the same sample complexity of the latter. In practice, estimating Σ first would consume the privacy budget from the mean estimation problem itself. On the other hand, the benefit of estimating Σ first is that one can obtain an error guarantee under the Mahalanobis distance [26], which cannot be achieved by our method. (2) By simply changing the primitive operations, our mechanism easily extends to the local and shuffle model of differential privacy. In doing so, we also extend the one-dimensional summation/mean estimation protocol in the shuffle model [5] to high dimensions.
In addition to the theoretical optimality, our mechanism is also simple and practical. Most importantly, there is no (internal) parameter to tune. Yet, our experimental results demonstrate that our mechanism outperforms the state-of-the-art algorithm [8] with the best parameters tuned for each specific setting.

heading: 1.3 Related Work
text: Asi and Duchi [4] recently initialized the study on instance optimality under DP. They propose two ways to relax (equivalently, strengthen the requirement on M ′) the strict instance optimality, which is unachievable. The first is to require M ′ to be unbiased. This is not appropriate for mean estimation, since many estimators, including clipped-mean, is not unbiased. The second is to require M ′ to work well over all the r-distance neighbors of D for r ≥ 1. Thus, their optimality is weaker than using Rnbr(·), hence not appropriate for the mean estimation problem (i.e., their optimality is the same as worst-case optimality). Instance optimality has not been studied in the local or shuffle model; existing protocols in these two models [7, 19, 5] all have errors proportional to the global sensitivity.
How to choose the clipping threshold C for the clipped mean estimator has been extensively studied [2, 3, 34, 32], but existing methods do not offer any optimality guarantees. In particular, Andrew et al. [3] also use a quantile (actually, median) as C, but as we shall see, median is actually not the optimal choice. Furthermore, they use online gradient descent to find a privatized quantile, which does not have any theoretical error guarantees. Amin et al. [2] attempt to select an optimal quantile as the clipping threshold to truncate the number of contributions from each user, instead of clipping the actual samples in high dimensions as in our paper.
In the statistical setting, where the data are i.i.d. samples from some specific distribution, there are numerous methods [26, 8, 27, 29] that can avoid an error proportional to the global sensitivity, by exploiting the concentration property of the distribution. In particular, Biswas et al. [8] provide a simple and practical mechanism for multivariate Gaussian data. Levy et al. [30] propose a private mean estimator with error scaling with the concentration radius τ of the distribution rather than the entire range, but their algorithm requires τ to be publicly known in advance. In the local model, the algorithm in [23] uses a quantile estimation procedure based on binary search as a subroutine for one-dimensional Gaussian data.
Very recently, the relationship between the error of mean estimation and the diameter of the dataset has been exploited in [15] for low-communication protocols, but they do not consider privacy. Our DP protocols in the local and shuffle models have communication cost Õ(d) per user (we do not state the communication costs in the theorems as they are not our major concern); it would be interesting to see if ideas from [15] can be used to reduce it further.

heading: 2 Preliminaries

heading: 2.1 Differential Privacy in the Central Model
text: Definition 1 (Differential Privacy (DP) [21]). For ε > 0 and δ ≥ 0, a randomized algorithm M : Xn → Y is (ε, δ)-differentially private if for any neighboring datasetsD ∼ D′ (i.e., dham(D,D′) = 1) and any E ⊆ Y , Pr[M(D) ∈ E] ≤ eε · Pr[M(D′) ∈ E] + δ. Definition 2 (Concentrated Differential Privacy (zCDP) [10]). For ρ > 0, a randomized algorithm M : Xn → Y is ρ-zCDP if for any D ∼ D′, Dα(M(D)||M(D′)) ≤ ρα for all α > 1, where Dα(M(D)||M(D′)) is the α-Rényi divergence between M(D) and M(D′).
Note that (ε, 0)-DP implies ε 2 2 -zCDP, which implies ( ε2 2 + ε √ 2 log 1δ , δ)-DP for any δ > 0. To release a numeric function f(D) taking values in Rd, the most common technique for achieving zCDP is by masking the result with Gaussian noise calibrated to the `2-sensitivity of f . Lemma 1 (Gaussian Mechanism [10]). Let f : Xn → Rd be a function with global `2-sensitivity GSf := maxD∼D′ ‖f(D) − f(D′)‖2. For a given data set D ∈ Xn, the mechanism that releases f(D) +N ( 0,
GS2f 2ρ · Id×d
) satisfies ρ-zCDP.
Lemma 2 (Composition Theorem [10, 21]). If M is an adaptive composition of differentially private algorithms M1,M2, . . . ,Mk, then
1. If each Mi satisfies (εi, δi)-DP, then M satisfies ( ∑ i εi, ∑ i δi)-DP.
2. For all ε, δ, δ′ ≥ 0, if each Mi satisfies (ε, δ)-DP, then M satisfies (ε′, kδ + δ′)-DP, where ε′ = √ 2k log 1δ′ ε+ kε(e ε − 1).
3. If each Mi satisfies ρi-zCDP, then M satisfies ( ∑ i ρi)-zCDP.

heading: 2.2 Differential Privacy in the Local Model and Shuffle Model
text: The above definitions of DP and zCDP assume that D is handled by a trusted curator and only the output of the mechanism will be released to the public. Therefore, if the curator is corrupted, the privacy of all users will be breached. For weaker trust assumptions, the most popular models are the local model and the shuffle model, where each user holds their datum and locally privatizes (by some randomized mechanism) the message before sending it out for analysis. Hence, there is no third-party who has direct access to D. Formally, each user holds one datum xi ∈ D, and the protocol interacts with the dataset using some local randomizer R : X → Y , and the privacy guarantee is defined over the transcript (all messages sent during the protocol). For simplicity, we only present the definition for one-round protocols; the privacy guarantee of multi-round protocols can be composed across all rounds by the composition theorem. The definition below uses zCDP; other DP notions can be defined similarly. Definition 3 (Local Model (LDP)). A protocol using R(·) as the local randomizer satisfies ρ-zCDP in the local model if for any x, x′ ∈ X , any α > 1, Dα(R(x)||R(x′)) ≤ ρα.
Due to the much stronger privacy requirement, the best accuracy guarantee of LDP protocols for several fundamental problems [12, 6, 18, 31] is a √ n-factor worse than that in the central model. The shuffle model is established on an intermediary level of trust assumption between the local model and the central model and aims for obtaining errors closer to the central model. The key feature of the shuffle model is a trusted shuffler S, which can permute all messages randomly before sending them to the analyzer, so that an adversary cannot identify the source of any message. Specifically, we consider the multi-message shuffle model, where each local randomizer R : X → Ym outputs m messages, and the transcript of the protocol ΠP (D) is a random permutation of all mn messages. The following definition uses (ε, δ)-DP; the other two DP notions can also be defined similarly, but they do not offer the improvements that we want over LDP protocols. Definition 4 (Shuffle Model). A protocol P satisfies (ε, δ)-DP in the shuffle model if for anyD ∼ D′, and any set E ⊆ Ymn, Pr[ΠP (D) ∈ E] ≤ eε · Pr[ΠP (D′) ∈ E] + δ.

heading: 3 Our Method

heading: 3.1 Clipped-Mean Estimator
text: In the rest of the paper, we focus on the mean function f(D) = 1n ∑n i=1 xi. Since GSf is large, a very natural idea is to clip each vector in its `2 norm by some threshold C. This reduces GSf to 2C/n, leading to the clipped-mean estimator [1]:
MC(D) = 1
n n∑ i=1 min { C ‖xi‖2 , 1 } · xi +N ( 0, 2C2 ρn2 I ) . (2)
Lemma 3. For any given C, MC(D) satisfies ρ-zCDP, and has an expected `2 error at most
E [‖MC(D)− f(D)‖2] ≤ E(C;D) := 1
n n∑ i=1 max{‖xi‖2 − C, 0}+ C n ·
√ 2d
ρ .
An important remaining question is how to set the clipping threshold C. Setting it too low will result in a large bias, while setting it too high will introduce a large amount of noise. We show how to choose the optimal C to balance this bias-variance trade-off. It is easy to see that the error E(C;D) is a convex function w.r.t. C, thus the optimal C can be found by setting the derivative of E(C;D) to zero, i.e.,
∂E(C;D) ∂C = 1 n |{i ∈ [n] | ‖xi‖2 > C}| − 1 n · √ 2d/ρ = 0.
Therefore, the optimal choice of C is the (n− √
2d/ρ)-th quantile of {‖xi‖2}i∈[n].

heading: 3.2 Private Quantile Selection
text: However, we cannot use the optimal C directly, as it would violate DP. Instead, we find a privatized quantile with small rank error. Specifically, for this problem, D consists of a sequence of ordered integers 0 ≤ x(1) ≤ · · · ≤ x(n) ≤ u. We would like to design a DP mechanism that, for a given m, returns an x (which is not necessarily an element in D) such that x(m−τ) ≤ x ≤ x(m+τ)1 w.h.p. Here τ is referred to as the rank error. Existing methods on private range counting queries [11, 20] can be used for this purpose, but they actually find all quantiles, which is an overkill. Instead, we use a simple binary search algorithm [25, 14], which not only simplifies the algorithm, but also reduces the rank error (by polylog(u) factors) to nearly optimal. Our algorithm PrivQuant makes use of a function NoisyRC([a, b],D) that returns a noisy count of |D ∩ [a, b]|, and we present the details in the supplementary material.
In the central DP model, we simply use NoisyRC([0,mid],D) = |D ∩ [0,mid]|+N (0, log u/(2ρ)). Theorem 1. The algorithm PrivQuant preserves ρ-CDP, and it returns a quantile with rank error τ with probability at least 1− β for τ = √ log u log log uβ /(2ρ).
In Section 4, we prove an Ω( √
log u/ρ) lower bound (Corollary 1) on the rank error under zCDP for constant β. Thus the algorithm is optimal up to just an O( √ log log u)-factor.
We can now use PrivQuant to find an approximately optimal clipping threshold. Specifically, we invoke PrivQuant with ρ′ = ρ/4 to find the max{n − max{ √ 2d/ρ, τ}, 1}-th quantile of {‖xi‖22}i∈[n]. They are integers no more than du2, so replacing u by du2 in Theorem 1 yields a rank error of τ = 2 √ log(du) log log(du)β /ρ. Then we set C̃ as the square root of the returned quantile. Finally, we return the clipped mean estimator MC̃(D) with ρ′ = 3ρ/4. The following theorem analyzes its error.
Theorem 2. Our mean estimation mechanism is ρ-zCDP and has `2 error O( √ d/ρ+ τ) · r(D)/n
with probability 1− β, where τ = 2 √
log(du) log log(du)β /ρ. 1Define x(j) = 0 for j < 1 and x(j) = u for j > n.

heading: 3.3 Shifted-Clipped-Mean Estimator
text: To reduce the error from being proportional to r(D) to being proportional to w(D), we perform a random rotation onD followed by a translation. The rotation is done by x̂i := HDxi, where H is the Hadamard matrix, D is a diagonal matrix whose diagonal entry is independently and uniformly drawn from {−1,+1}. Note that for now we omit the normalization coefficient 1√
d so that each coordinate
of x̂i is still an integer; we will apply the normalization to the final estimator instead. Then, for each j ∈ [d], we invoke PrivQuant with ρ′ = ρ/(4d) to find an approximate median of {x̂i}i∈[n] along dimension j, denoted as c̃j . Next, we shift the dataset to be centered around c̃ = (c̃1, . . . , c̃d), obtaining D̃ = {x̃i := x̂i− c̃}i∈[n]. Note that c̃ has integer coordinates, so does x̃i. Finally, we apply the clipped-mean estimator in Theorem 2 with ρ′ = 34ρ on D̃, obtaining an estimation ỹ, and return y := ( 1√
d HD)−1 1√ d (ỹ + c̃) as the mean estimator over D. Theorem 3. Set τ = √ log(du) log d log(du)β /ρ and assume n = Ω(τ √ d). Our mean estimation
mechanism is ρ-zCDP, and has `2 error O ( ( √ d/ρ+ τ) √ log ndβ ) ·w(D)/n with probability 1− β.
Remark 1. For a dataset with real coordinates bounded by R (in absoluate value), one can quantize each coordinate to an integer using bucket size α/ √ d, for any 0 < α < R, and then apply our algorithm over an integer universe of size u = 2R √ d/α. This just brings an additive α error to the error bound of Theorem 3.
Statistical Mean Estimation. Suppose D consists of i.i.d. samples drawn from the multivariate Gaussian distribution N (µ,Σ), and we wish to estimate µ, assuming a priori bounds ‖µ‖2 ≤ R and σ2minI Σ σ2maxI. We first clip each sample xi ← xi · min{R′/‖xi‖2, 1} where R′ := R + 2σmax √ d+ log 4nβ . Then we apply our mechanism with bucket size α/ √ d where
α = σmin √ d/n. We analyze the error in the supplementary material. When Σ = I and ignoring
logO(1)(dnRβ · σmax σmin
) factors, the error in becomes Õ (√
d√ n + d√ρn
) , matching the known optimal
bound for Gaussian mean estimation [8].

heading: 4 Lower Bounds
text: In this section we establish the instance optimality of Theorem 3 via three lower bounds: (1) Rin-nbr(D) = Ω(w(D)/n) for all D; (2) an Ω̃( √ d/ρ) lower bound on the optimality ratio, and (3)
that the condition n = Ω̃( √ d/ρ) is necessary.
The first lower bound follows from an observation by Vadhan [35]:
Lemma 4 ([35]). For any f , any (ε, δ)-DP mechanism M ′, and any neighboring datasets D0 ∼ D1, there is a b ∈ {0, 1} such that
Pr[‖M ′(Db)− f(Db)‖2 < ‖f(D0)− f(D1)‖2/2] < 1 + δ
1 + e−ε .
Theorem 4. For ε < 0.1, δ < 0.1,Rin-nbr(D) = Ω(w(D)/n).
The lower bound on the optimality ratio is by the reduction from statistical mean estimation [26].
Theorem 5. LetM be any ρ-zCDP mechanism for mean estimation that has `2 error c ·w(D)/n with constant probability for any D = {xi}i∈[n] drawn from [u]d. If ρ < Õ( √ d/n), then c = Ω̃( √ d/ρ).
For the lower bound on n, we consider a weaker problem (so the lower bound is stronger), which is the d-dimensional version of the interior point problem [9]: Given a dataset D = {xi}i∈[n] drawn from [u]d, the mechanism is only required to return a y ∈ [u]d such that mini xij ≤ yj ≤ maxi xij for all j with constant probability.
Theorem 6. If there exists a ρ-zCDP mechanism that solves the interior point problem with success probability 2/3, then n = Ω( √ d log u/ρ).
Given a quantile selection mechanism with rank error τ , by finding the median, the 1-dimensional interior point problem can be solved when n = O(τ). The following corollary then follows from Theorem 6. Corollary 1. Any ρ-zCDP mechanism for the quantile selection problem must have rank error Ω( √ log u/ρ).

heading: 5 Extension to the Local Model and Shuffle Model
text: Our mean estimation framework can be summarized as follows: (1) Given D = {x1, . . . , xn}, perform a random rotation, obtaining D̂ = {x̂i := HDxi}i∈[n]; (2) For each j ∈ [d], find an approximate median c̃j of D̂ along dimension j. Shift D̂ to be centered around c̃, obtaining D̃ = {x̃i := x̂i − c̃}; (3) Find a clipping threshold C, which is the m-th quantile over the `2 norms of the vectors in D̃. In the central model, the optimal choice is m = n − √ 2d/ρ; (4) Perform
`2 clipping over D̃ using C, and obtain a mean estimator ỹ of the clipped vectors. Finally, return y = ( 1√
d HD)−1 1√ d (ỹ + c̃).
We note that each step has their counterparts in the local and the shuffle model: Step (1) is easy, where the randomized diagonal matrix D can be generated using public randomness, or sent from the aggregator to each user if public randomness is not available. Step (2) and (3) both rely on quantile selection, which have alternatives in the local model and shuffle model. Step (4) is also easy since all vectors have norms bounded by C. We elaborate on the details in the supplementary material.
The Local Model. For step (4), the standard LDP mechanism is that each user applies the Gaussian mechanism (Lemma 1) with GS = 2C to inject noise to their clipped vector, sends it out, and the aggregator adds them up and divides the sum by n. For quantile selection in step (2) and (3), we use the LDP range counting protocol in [13], which returns a data structure such that any range counting query can be answered. Putting things together, we obtain the following result.
Theorem 7. Set τ = √ n/ρ log2(du) log duβ and assume n = Ω(τ √ d). There is a 3-round ρ-zCDP
mean estimation mechanism in the local model, achieving an `2-error ofO ( ( √ dn/ρ+ τ) √ log ndβ ) · w(D)/n with probability 1− β.
The Shuffle Model. We see that the error in the local model is worse than that in the central model by a √ n-factor. It turns out that in the shuffle model, we can match the result in the central model up to logarithmic factors, albeit with (ε, δ)-DP. This is mostly due to highly accurate summation and range counting protocols discovered recently for the shuffle model. We first extend the one-dimensional summation protocol in [5] for high-dimensional data by using random rotation. Lemma 5. Given D = {xi}i∈[n] ⊂ Rd where ‖xi‖2 ≤ C for all i, there is a oneround (ε, δ)-DP mean estimation protocol in the shuffle model that returns a y such that
Pr [ ‖y − f(D)‖2 ≤ O ( C εn √ log(nd) log dδ )] ≥ 2/3.
For the NoisyRC queries in the algorithm PrivQuant, we can use the range counting mechanism [24] in the shuffle model. Putting things together, we obtain the following result.
Theorem 8. Set τ = 1ε log 3.5(du) √ log d log(du)δ and assume n = Ω ( τ √ d log dδ ) . There is
a 3-round (ε, δ)-DP mean estimation mechanism in the shuffle model, achieving an `2-error of
O (( 1 ε √ d log dδ + τ )√ log(nd) ) · w(D)/n with probability 2/3.

heading: 6 Experiments
text: We performed both statistical and empirical mean estimation experiments to evaluate our method. For statistical mean estimation, we used multivariate Gaussian distributions with various µ and Σ. All algorithms are given the same R, σmin, σmax. We tried various R, while fixing σmin = 0.1 and σmax = R/ √ d. For empirical mean estimation, we used a real-world dataset, MNIST, which consists
of 70,000 images of handwritten digits, where each image is represented by a vector of dimension d = 784 = 28× 28. We quantized the values to integers [u] for u = 210. We measured the `2 error by taking the trimmed mean with trimming parameter 0.1 over 100 trials (as in [8]).
For the central model, we compared with COINPRESS2 [8]. It starts with a given confidence ball of radius R that captures the mean, and iteratively refines the confidence ball. The number of iterations t is an important internal parameter in this algorithm; we tried t = 1, 2, 3, 4, 10 following their suggestion.
The results for statistical mean estimation are shown in Fig. 1 to 5, where the detailed parameter settings are given in the captions. The results show that our method (Shifted-CM) performs at least as well as COINPRESS with the best t across a variety of settings. In particular, when Σ is not identity Id×d, our method significantly outperforms COINPRESS and offers errors close to the non-private estimator as demonstrated in Fig. 3 and Fig. 4. Moreover, as seen from Fig. 3, 4, and 5, the best choice of t for COINPRESS is quite sensitive to Σ and R, making it difficult to tune in practice.
2We also compared with another version of COINPRESS in the supplementary material, where we first scale the data by an estimated Σ (obtained by MVCRec [8] using half of the privacy budget ρ).
Both our method and COINPRESS are translation-invariant. This can be verified from Fig. 1, 2, and 5, where the results are not effected by µ. However, the approaches taken are different: COINPRESS uses an iterative process, while we shift the dataset to be centered around an approximate center point. In Fig. 1 and 2, we also include CM, our estimator without this shift operation, which is indeed affected by µ.
For empirical mean estimation on the MNIST dataset, we see in Fig. 6 that our method outperforms COINPRESS for various privacy levels. This means that this dataset, as with most real-world datasets, does not follow Gaussian distribution with an identity Σ. The instance-optimality of our method is precisely the reason behind its robustness to different distributional assumptions, or the lack of. One important component of our method is the optimal clipping threshold C, which is the (n −√ 2d/ρ)-th quantile of the norms. Note that this depends on d and ρ. Prior work [3] used a fixed quantile (e.g., the median). To better see the relationship between the optimal C and d, ρ, we used a synthetic dataset D = {i · 1d}i∈[n] with n = 500 and tried different quantiles as the clipping threshold. The results in Fig. 7 confirm our theoretical analysis: The optimal C indeed depends on d and ρ, while our choice attains the optimum.
We also evaluated our algorithm in the local model and compare it with [23], and the results are presented in the supplementary material.
Acknowledgments and Disclosure of Funding
We would like to thank Gautam Kamath and Jonathan Ullman for helpful discussions about CoinPress [8].
Funding in direct support of this work: This work is supported by HKRGC under grants 16201318, 16201819, and 16205420.

Review summaries:
The paper proposes a new algorithm for the mean estimation problem that gets around the "curse of global sensitivity" and achieves the so-called instance optimality. Experiments including a real-data one are carried out to verify the practicality. The results are also extended to the shuffle and local models of DP.

This paper does differentially private mean estimation for empirical estimation, as opposed to statistical estimation (statistical estimation is an application of their work). It focusses on instance optimal mean estimation by introducing a new target error function of the dataset, $\mathcal{R}_{\text{in-nbr}}$. It shows both and upper bound and a lower bound for this problem. It has both theoretical and empirical results.  The theoretical results say that for this measure of error, the optimal error scales according to the diameter of the dataset in question. To prove the upper bound, the authors first use a clipped-mean-estimator, with the clipping threshold decided via a simple derivative of the error. Since that clipping estimate is a function of the dataset, it could not be used directly. So, they came up with a simple quantile estimator to do the same. They, however, first performed a random rotation, followed by a translation to fix scaling issues. Then they finally use the aforementioned clipped-mean-estimator.  They finally showed application in statistical mean estimation of high-dimensional Gaussians. They showed that they require very weak prior knowledge of the covariance matrix of the Gaussian for this. They finally compared their empirical results with the Coinpress algorithm of Biswas et al.  They extend these results to the local and shuffle models of DP, too.

This paper considers the problem of private mean estimation and attempts to develop algorithms that are instance-optimal, that is, adapting to the difficulty of the underlying instance. The authors modify the existing instance-optimality definition in differential privacy, and develop an algorithm that is nearly instance-optimal. The caveat here is that nearly instance-optimal here hides factors that depend on the dimension and privacy parameters, hence making the optimality guarantee less strong than what claimed by the authors.

The paper studies instance-optimal mean estimation in the central model of differential privacy, with straightforward extensions to the shuffle and local models. The specific notion of instance optimality used is, I believe, original to this paper: the goal is to find an algorithm minimizing error over a given database D and a restricted neighborhood of databases that are neighbors and only include elements also present in D. The algorithm consists of four steps:  1) randomly rotate the database, 2) center it using a privately estimated component-wise median, 3) privately estimate a certain quantile of the L_2 norm of the resulting database and clip data to that estimated L_2 norm, 4) take a noisy empirical mean of the clipped data and de-bias.  The paper gives instance-specific upper and lower bounds that are nearly matching, up to logarithmic factors in the dimension and a discretization parameter. Experiments suggest that the algorithm performs well against existing methods.