International Association for Cryptologic Research

International Association
for Cryptologic Research


Uri Stemmer


MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably
In the current digital world, large organizations (sometimes referred to as tech giants) provide service to extremely large numbers of users. The service provider is often interested in computing various data analyses over the private data of its users, which in turn have their incentives to cooperate, but do not necessarily trust the service provider. In this work, we introduce the \emph{Gulliver multi-party computation model} (GMPC) to realistically capture the above scenario. The GMPC model considers a single highly powerful party, called the {\em server} or {\em Gulliver}, that is connected to $n$ users over a star topology network (alternatively formulated as a full network, where the server can block any message). The users are significantly less powerful than the server, and, in particular, should have both computation and communication complexities that are polylogarithmic in $n$. Protocols in the GMPC model should be secure against malicious adversaries that may corrupt a subset of the users and/or the server. Designing protocols in the GMPC model is a delicate task, since users can only hold information about $\polylog(n)$ other users (and, in particular, can only communicate with $\polylog(n)$ other users). In addition, the server can block any message between any pair of honest parties. Thus, reaching an agreement becomes a challenging task. Nevertheless, we design generic protocols in the GMPC model, assuming that at most $\alpha<1/8$ fraction of the users may be corrupted (in addition to the server). Our main contribution is a variant of Feige's committee election protocol [FOCS 1999] that is secure in the GMPC model. Given this tool we show: 1. Assuming fully homomorphic encryption (FHE), any computationally efficient function with $O(n\cdot\polylog(n))$-size output can be securely computed in the GMPC model. 2. Any function that can be computed by a circuit of $O(\polylog(n))$ depth, $O(n\cdot\polylog(n))$ size, and bounded fan-in and fan-out can be securely computed in the GMPC model assuming vector commitment schemes (without assuming FHE). 3. In particular, {\em sorting} can be securely computed in the GMPC model assuming vector commitment schemes. This has important applications for the {\em shuffle model of differential privacy}, and resolves an open question of Bell et al. [CCS 2020].
On Differential Privacy and Adaptive Data Analysis with Bounded Space
We study the space complexity of the two related fields of {\em differential privacy} and {\em adaptive data analysis}. Specifically, \begin{enumerate} \item Under standard cryptographic assumptions, we show that there exists a problem $P$ that requires exponentially more space to be solved efficiently with differential privacy, compared to the space needed without privacy. To the best of our knowledge, this is the first separation between the space complexity of private and non-private algorithms. \item The line of work on adaptive data analysis focuses on understanding the number of {\em samples} needed for answering a sequence of adaptive queries. We revisit previous lower bounds at a foundational level, and show that they are a consequence of a space bottleneck rather than a sampling bottleneck. \end{enumerate} To obtain our results, we define and construct an encryption scheme with multiple keys that is built to withstand a limited amount of key leakage in a very particular way.
Separating Adaptive Streaming from Oblivious Streaming using the Bounded Storage Model 📺
Streaming algorithms are algorithms for processing large data streams, using only a limited amount of memory. Classical streaming algorithms typically work under the assumption that the input stream is chosen independently from the internal state of the algorithm. Algorithms that utilize this assumption are called oblivious algorithms. Recently, there is a growing interest in studying streaming algorithms that maintain utility also when the input stream is chosen by an adaptive adversary, possibly as a function of previous estimates given by the streaming algorithm. Such streaming algorithms are said to be adversarially-robust. By combining techniques from learning theory with cryptographic tools from the bounded storage model, we separate the oblivious streaming model from the adversarially-robust streaming model. Specifically, we present a streaming problem for which every adversarially-robust streaming algorithm must use polynomial space, while there exists a classical (oblivious) streaming algorithm that uses only polylogarithmic space. This is the first general separation between the capabilities of these two models, resolving one of the central open questions in adversarial robust streaming.
On the Round Complexity of the Shuffle Model 📺
The shuffle model of differential privacy [Bittau et al. SOSP 2017; Erlingsson et al. SODA 2019; Cheu et al. EUROCRYPT 2019] was proposed as a viable model for performing distributed differentially private computations. Informally, the model consists of an untrusted analyzer that receives messages sent by participating parties via a shuffle functionality, the latter potentially disassociates messages from their senders. Prior work focused on one-round differentially private shuffle model protocols, demonstrating that functionalities such as addition and histograms can be performed in this model with accuracy levels similar to that of the curator model of differential privacy, where the computation is performed by a fully trusted party. A model closely related to the shuffle model was presented in the seminal work of Ishai et al. on establishing cryptography from anonymous communication [FOCS 2006]. Focusing on the round complexity of the shuffle model, we ask in this work what can be computed in the shuffle model of differential privacy with two rounds. Ishai et al. showed how to use one round of the shuffle to establish secret keys between every two parties. Using this primitive to simulate a general secure multi-party protocol increases its round complexity by one. We show how two parties can use one round of the shuffle to send secret messages without having to first establish a secret key, hence retaining round complexity. Combining this primitive with the two-round semi-honest protocol of Applebaum, Brakerski, and Tsabary [TCC 2018], we obtain that every randomized functionality can be computed in the shuffle model with an honest majority, in merely two rounds. This includes any differentially private computation. We hence move to examine differentially private computations in the shuffle model that (i) do not require the assumption of an honest majority, or (ii) do not admit one-round protocols, even with an honest majority. For that, we introduce two computational tasks: common element, and nested common element with parameter $\alpha$. For the common element problem we show that for large enough input domains, no one-round differentially private shuffle protocol exists with constant message complexity and negligible $\delta$, whereas a two-round protocol exists where every party sends a single message in every round. For the nested common element we show that no one-round differentially private protocol exists for this problem with adversarial coalition size $\alpha n$. However, we show that it can be privately computed in two rounds against coalitions of size $cn$ for every $c < 1$. This yields a separation between one-round and two-round protocols. We further show a one-round protocol for the nested common element problem that is differentially private with coalitions of size smaller than $c n$ for all $0 < c < \alpha < 1 / 2$.

Program Committees

TCC 2020