## Contact

## Office Location

805 Columbus Avenue

623 Interdisciplinary Science and Engineering Complex (ISEC)

Boston, MA 02120

## Mailing Address

Northeastern University

ATTN: Jonathan Ullman, 435 ISEC

360 Huntington Avenue

Boston, MA 02115

## Research Interests

- Privacy
- Machine learning and statistics
- Cryptography
- Algorithms

## Education

- PhD in computer science, Harvard University
- BSE in computer science, Princeton University

## Biography

Jonathan Ullman is a theoretical computer scientist. The focus of his research is how to make data analysis more reliable and better aligned with societal values. A particular focus is statistical data privacy, which studies how and when we can analyze a dataset without revealing information about the individuals in that dataset. He is also interested in how to prevent false discovery in the empirical sciences. He studies these and other questions using tools from cryptography, algorithms, machine learning and statistics.

His research has been recognized with an NSF CAREER Award and a Google Faculty Research Award.

## Recent Publications

### Differentially Private Fair Learning

Jagielski, Matthew, Kearns, Michael, Mao, Jieming, Oprea, Alina, Roth, Aaron, Sharifi, Saeed, & Ullman, Jonathan. (2019). Differentially Private Fair Learning. Proceedings of the 36 Th International Conference on Machine Learning.

## Abstract

Motivated by settings in which predictive models may be required to be non-discriminatory with respect to certain attributes (such as race), but even collecting the sensitive attribute may be forbidden or restricted, we initiate the study of fair learning under the constraint of differential privacy. We design two learning algorithms that simultaneously promise differential privacy and equalized odds, a ‘fairness’ condition that corresponds to equalizing false positive and negative rates across protected groups. Our first algorithm is a private implementation of the equalized odds post-processing approach of [Hardt et al., 2016]. This algorithm is appealingly simple, but must be able to use protected group membership explicitly at test time, which can be viewed as a form of ‘disparate treatment’. Our second algorithm is a differentially private version of the oracle-efficient in-processing approach of [Agarwal et al., 2018] that can be used to find the optimal fair classifier, given access to a subroutine that can solve the original (not necessarily fair) learning problem. This algorithm is more complex but need not have access to protected group membership at test time. We identify new tradeoffs between fairness, accuracy, and privacy that emerge only when requiring all three properties, and show that these tradeoffs can be milder if group membership may be used at test time. We conclude with a brief experimental evaluation.

### Privately Learning High-Dimensional Distributions

Kamath, G., Li, J., Singhal, V., & Ullman, J. (2018). Privately Learning High-Dimensional Distributions. COLT.

## Abstract

We present novel, computationally efficient, and differentially private algorithms for two fundamental high-dimensional learning problems: learning a multivariate Gaussian and learning a product distribution over the Boolean hypercube in total variation distance. The sample complexity of our algorithms nearly matches the sample complexity of the optimal non-private learners for these tasks in a wide range of parameters, showing that privacy comes essentially for free for these problems. In particular, in contrast to previous approaches, our algorithm for learning Gaussians does not require strong a priori bounds on the range of the parameters. Our algorithms introduce a novel technical approach to reducing the sensitivity of the estimation procedure that we call recursive private preconditioning.

### The Structure of Optimal Private Tests for Simple Hypotheses

Canonne, C.L., Kamath, G., McMillan, A., Smith, A.D., & Ullman, J. (2018). The structure of optimal private tests for simple hypotheses. ArXiv, abs/1811.11148.

## Abstract

Hypothesis testing plays a central role in statistical inference, and is used in many settings where privacy concerns are paramount. This work answers a basic question about privately testing simple hypotheses: given two distributions P and Q, and a privacy level ε, how many i.i.d. samples are needed to distinguish P from Q subject to ε-differential privacy, and what sort of tests have optimal sample complexity? Specifically, we characterize this sample complexity up to constant factors in terms of the structure of P and Q and the privacy level ε, and show that this sample complexity is achieved by a certain randomized and clamped variant of the log-likelihood ratio test. Our result is an analogue of the classical Neyman-Pearson lemma in the setting of private hypothesis testing. We also give an application of our result to the private change-point detection. Our characterization applies more generally to hypothesis tests satisfying essentially any notion of algorithmic stability, which is known to imply strong generalization bounds in adaptive data analysis, and thus our results have applications even when privacy is not a primary concern.

### Distributed Differential Privacy via Shuffling

Cheu A., Smith A., Ullman J., Zeber D., Zhilyaev M. (2019) Distributed Differential Privacy via Shuffling. In: Ishai Y., Rijmen V. (eds) Advances in Cryptology – EUROCRYPT 2019. EUROCRYPT 2019. Lecture Notes in Computer Science, vol 11476. Springer, Cham

## Abstract

We consider the problem of designing scalable, robust protocols for computing statistics about sensitive data. Specifically, we look at how best to design differentially private protocols in a distributed setting, where each user holds a private datum. The literature has mostly considered two models: the “central” model, in which a trusted server collects users’ data in the clear, which allows greater accuracy; and the “local” model, in which users individually randomize their data, and need not trust the server, but accuracy is limited. Attempts to achieve the accuracy of the central model without a trusted server have so far focused on variants of cryptographic MPC, which limits scalability.

In this paper, we initiate the analytic study of a shuffled model for distributed differentially private algorithms, which lies between the local and central models. This simple-to-implement model, a special case of the ESA framework of [Bittau et al., ’17], augments the local model with an anonymous channel that randomly permutes a set of user-supplied messages. For sum queries, we show that this model provides the power of the central model while avoiding the need to trust a central server and the complexity of cryptographic secure function evaluation. More generally, we give evidence that the power of the shuffled model lies strictly between those of the central and local models: for a natural restriction of the model, we show that shuffled protocols for a widely studied selection problem require exponentially higher sample complexity than do central-model protocols.

### Algorithmic stability for adaptive data analysis

Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman. Algorithmic stability for adaptive data analysis. In Symposium on Theory of Computing (STOC’16), 2016

**Abstract**

Adaptivity is an important feature of data analysis – the choice of questions to ask about a dataset often depends on previous interactions with the same dataset. However, statistical validity is typically studied in a nonadaptive model, where all questions are specified before the dataset is drawn. Recent work by Dwork et al. (STOC, 2015) and Hardt and Ullman (FOCS, 2014) initiated a general formal study of this problem, and gave the first upper and lower bounds on the achievable generalization error for adaptive data analysis.

Specifically, suppose there is an unknown distribution **P** and a set of *n* independent samples **x** is drawn from **P**. We seek an algorithm that, given **x** as input, accurately answers a sequence of adaptively chosen “queries” about the unknown distribution **P**. How many samples *n*must we draw from the distribution, as a function of the type of queries, the number of queries, and the desired level of accuracy?

In this work we make two new contributions towards resolving this question:

- We give upper bounds on the number of samples
*n*that are needed to answer*statistical queries*. The bounds improve and simplify the work of Dwork et al. (STOC, 2015), and have been applied in subsequent work by those authors (Science, 2015; NIPS, 2015). - We prove the first upper bounds on the number of samples required to answer more general families of queries. These include arbitrary
*low-sensitivity queries*and an important class of*optimization queries*(alternatively,*risk minimization queries*).

As in Dwork et al., our algorithms are based on a connection with *algorithmic stability* in the form of *differential privacy*. We extend their work by giving a quantitatively optimal, more general, and simpler proof of their main theorem that the stability notion guaranteed by differential privacy implies low generalization error. We also show that weaker stability guarantees such as bounded KL divergence and total variation distance lead to correspondingly weaker generalization guarantees.

### Interactive fingerprinting codes and the hardness of preventing false discovery

Thomas Steinke and Jonathan Ullman. Interactive fingerprinting codes and the hardness of preventing false discovery. In Proceedings of The 28th Conference on Learning Theory (COLT’15), 2015

**Abstract**

We show an essentially tight bound on the number of adaptively chosen statistical queries that a computationally efficient algorithm can answer accurately given n samples from an unknown distribution. A statistical query asks for the expectation of a predicate over the underlying distribution, and an answer to a statistical query is accurate if it is “close” to the correct expectation over the distribution. This question was recently studied by Dwork et al., who showed how to answer Ω~(n2) queries efficiently, and also by Hardt and Ullman, who showed that answering O~(n3) queries is hard. We close the gap between the two bounds and show that, under a standard hardness assumption, there is no computationally efficient algorithm that, given nsamples from an unknown distribution, can give valid answers to O(n2) adaptively chosen statistical queries. An implication of our results is that computationally efficient algorithms for answering arbitrary, adaptively chosen statistical queries may as well be differentially private.

We obtain our results using a new connection between the problem of answering adaptively chosen statistical queries and a combinatorial object called an interactive fingerprinting code. In order to optimize our hardness result, we give a new Fourier-analytic approach to analyzing fingerprinting codes that is simpler, more flexible, and yields better parameters than previous constructions.

### Tight lower bounds for differentially private selection

Thomas Steinke and Jonathan Ullman. Tight lower bounds for differentially private selection. In IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS’17), 2017.

**Abstract**

A pervasive task in the differential privacy literature is to select the k items of “highest quality” out of a set of d items, where the quality of each item depends on a sensitive dataset that must be protected. Variants of this task arise naturally in fundamental problems like feature selection and hypothesis testing, and also as subroutines for many sophisticated differentially private algorithms.

The standard approaches to these tasks—repeated use of the exponential mechanism or the sparse vector technique—approximately solve this problem given a dataset of n=O(k−−√logd) samples. We provide a tight lower bound for some very simple variants of the private selection problem. Our lower bound shows that a sample of size n=Ω(k−−√logd) is required even to achieve a very minimal accuracy guarantee.

Our results are based on an extension of the fingerprinting method to sparse selection problems. Previously, the fingerprinting method has been used to provide tight lower bounds for answering an entire set of d queries, but often only some much smaller set of k queries are relevant. Our extension allows us to prove lower bounds that depend on both the number of relevant queries and the total number of queries.

### Robust traceability from trace amounts

Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan. Robust traceability from trace amounts. In IEEE 56th Annual Symposium on Foundations of Computer Science (FOCS’15), 2015.

**Abstract**

The privacy risks inherent in the release of a large number of summary statistics were illustrated by Homer et al. (PLoS Genetics, 2008), who considered the case of 1-way marginals of SNP allele frequencies obtained in a genome-wide association study: Given a large number of minor allele frequencies from a case group of individuals diagnosed with a particular disease, together with the genomic data of a single target individual and statistics from a sizable reference dataset independently drawn from the same population, an attacker can determine with high confidence whether or not the target is in the case group. In this work we describe and analyze a simple attack that succeeds even if the summary statistics are significantly distorted, whether due to measurement error or noise intentionally introduced to protect privacy. Our attack only requires that the vector of distorted summary statistics is close to the vector of true marginals in ℓ_{1} norm. Moreover, the reference pool required by previous attacks can be replaced by a single sample drawn from the underlying population. The new attack, which is not specific to genomics and which handles Gaussian as well as Bernoulli data, significantly generalizes recent lower bounds on the noise needed to ensure differential privacy (Bun, Ullman, and Vadhan, STOC 2014, Steinke and Ullman, 2015), obviating the need for the attacker to control the exact distribution of the data.

## Khoury PhD Students & Post-Docs

PhD Students

### Albert Cheu

PhD Student

PhD Students

### Vikrant Singhal

PhD Student

PhD Students

### Lydia Zakynthinou

PhD Student