International Association for Cryptologic Research

International Association
for Cryptologic Research


Ameya Velingker


On the power of multiple anonymous messages: Frequency Estimation and Selection in the Shuffle Model of Differential Privacy 📺
It is well-known that general secure multi-party computation can in principle be applied to implement differentially private mechanisms over distributed data with utility matching the curator (a.k.a. central) model. In this paper we study the power of protocols running on top of a much weaker primitive: A non-interactive anonymous channel, known as the shuffled model in the differential privacy literature. Such protocols are implementable in a scalable way using known cryptographic methods and are known to enable non-interactive, differentially private protocols with error much smaller than what is possible in the local model. We study fundamental counting problems in the shuffled model and obtain tight, up to poly-logarithmic factors, bounds on the error and communication in several settings. For the problem of frequency estimation for n users and a domain of size B, we obtain: - A nearly tight lower bound of ˜Ω(min(n^(1/4), sqrt(B))) on the error in the single-message shuffled model. This implies that the protocols obtained from the amplification via shuffling work of Erlingsson et al. (SODA 2019) and Balle et al. (Crypto 2019) are essentially optimal for single-message protocols. - Protocols in the multi-message shuffled model with poly(log B, log n) bits of communication per user and poly log B error, which provide an exponential improvement on the error compared to what is possible with single-message algorithms. This implies protocols with similar error and communication guarantees for several well-studied problems such as heavy hitters, d-dimensional range counting, M-estimation of the median and quantiles, and more generally sparse non-adaptive statistical query algorithms. For the related selection problem on a domain of size B, we prove: - A nearly tight lower bound of Ω(B) on the number of users in the single-message shuffled model. This significantly improves on the Ω(B^(1/17)) lower bound obtained by Cheu et al. (Eurocrypt 2019). A key ingredient in the proof is a lower bound on the error of locally-private frequency estimation in the low-privacy (aka high ε) regime. For this we develop new techniques to extend the results of Duchi et al. (FOCS 2013; JASA 2018) and Bassily & Smith (STOC 2015), whose techniques only gave tight bounds in the high-privacy setting.
Private Aggregation from Fewer Anonymous Messages 📺
Consider the setup where $n$ parties are each given an element~$x_i$ in the finite field $\F_q$ and the goal is to compute the sum $\sum_i x_i$ in a secure fashion and with as little communication as possible. We study this problem in the \emph{anonymized model} of Ishai et al.~(FOCS 2006) where each party may broadcast anonymous messages on an insecure channel. We present a new analysis of the one-round ``split and mix'' protocol of Ishai et al. In order to achieve the same security parameter, our analysis reduces the required number of messages by a $\Theta(\log n)$ multiplicative factor. We also prove lower bounds showing that the dependence of the number of messages on the domain size, the number of parties, and the security parameter is essentially tight. Using a reduction of Balle et al. (2019), our improved analysis of the protocol of Ishai et al. yields, in the same model, an $\left(\varepsilon, \delta\right)$-differentially private protocol for aggregation that, for any constant $\varepsilon > 0$ and any $\delta = \frac{1}{\poly(n)}$, incurs only a constant error and requires only a \emph{constant number of messages} per party. Previously, such a protocol was known only for $\Omega(\log n)$ messages per party.