Úlfar Erlingsson

Nicolas Papernot, Abhradeep Thakurta, Shuang Song et al.

Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer privacy for training data. In practice, this has been mostly an afterthought, with privacy-preserving models obtained by re-running training with a different optimizer, but using the model architectures that already performed well in a non-privacy-preserving setting. This approach leads to less than ideal privacy/utility tradeoffs, as we show here. Instead, we propose that model architectures are chosen ab initio explicitly for privacy-preserving training. To provide guarantees under the gold standard of differential privacy, one must bound as strictly as possible how individual training points can possibly affect model updates. In this paper, we are the first to observe that the choice of activation function is central to bounding the sensitivity of privacy-preserving deep learning. We demonstrate analytically and experimentally how a general family of bounded activation functions, the tempered sigmoids, consistently outperform unbounded activation functions like ReLU. Using this paradigm, we achieve new state-of-the-art accuracy on MNIST, FashionMNIST, and CIFAR10 without any modification of the learning procedure fundamentals or differential privacy analysis.

Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov et al.

Recently, a number of approaches and techniques have been introduced for reporting software statistics with strong privacy guarantees. These range from abstract algorithms to comprehensive systems with varying assumptions and built upon local differential privacy mechanisms and anonymity. Based on the Encode-Shuffle-Analyze (ESA) framework, notable results formally clarified large improvements in privacy guarantees without loss of utility by making reports anonymous. However, these results either comprise of systems with seemingly disparate mechanisms and attack models, or formal statements with little guidance to practitioners. Addressing this, we provide a formal treatment and offer prescriptive guidelines for privacy-preserving reporting with anonymity. We revisit the ESA framework with a simple, abstract model of attackers as well as assumptions covering it and other proposed systems of anonymity. In light of new formal privacy bounds, we examine the limitations of sketch-based encodings and ESA mechanisms such as data-dependent crowds. We also demonstrate how the ESA notion of fragmentation (reporting data aspects in separate, unlinkable messages) improves privacy/utility tradeoffs both in terms of local and central differential-privacy guarantees. Finally, to help practitioners understand the applicability and limitations of privacy-preserving reporting, we report on a large number of empirical experiments. We use real-world datasets with heavy-tailed or near-flat distributions, which pose the greatest difficulty for our techniques; in particular, we focus on data drawn from images that can be easily visualized in a way that highlights reconstruction errors. Showing the promise of the approach, and of independent interest, we also report on experiments using anonymous, privacy-preserving reporting to train high-accuracy deep neural networks on standard tasks---MNIST and CIFAR-10.

Nicholas Carlini, Úlfar Erlingsson, Nicolas Papernot

We develop techniques to quantify the degree to which a given (training or testing) example is an outlier in the underlying distribution. We evaluate five methods to score examples in a dataset by how well-represented the examples are, for different plausible definitions of "well-represented", and apply these to four common datasets: MNIST, Fashion-MNIST, CIFAR-10, and ImageNet. Despite being independent approaches, we find all five are highly correlated, suggesting that the notion of being well-represented can be quantified. Among other uses, we find these methods can be combined to identify (a) prototypical examples (that match human expectations); (b) memorized training examples; and, (c) uncommon submodes of the dataset. Further, we show how we can utilize our metrics to determine an improved ordering for curriculum learning, and impact adversarial robustness. We release all metric values on training and test sets we studied.

Úlfar Erlingsson, Ilya Mironov, Ananth Raghunathan et al.

The guarantees of security and privacy defenses are often strengthened by relaxing the assumptions made about attackers or the context in which defenses are deployed. Such relaxations can be a highly worthwhile topic of exploration---even though they typically entail assuming a weaker, less powerful adversary---because there may indeed be great variability in both attackers' powers and their context. However, no weakening or contextual discounting of attackers' power is assumed for what some have called "relaxed definitions" in the analysis of differential-privacy guarantees. Instead, the definitions so named are the basis of refinements and more advanced analyses of the worst-case implications of attackers---without any change assumed in attackers' powers. Because they more precisely bound the worst-case privacy loss, these improved analyses can greatly strengthen the differential-privacy upper-bound guarantees---sometimes lowering the differential-privacy epsilon by orders-of-magnitude. As such, to the casual eye, these analyses may appear to imply a reduced privacy loss. This is a false perception: the privacy loss of any concrete mechanism cannot change with the choice of a worst-case-loss upper-bound analysis technique. Practitioners must be careful not to equate real-world privacy with differential-privacy epsilon values, at least not without full consideration of the context.

Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov et al.

Sensitive statistics are often collected across sets of users, with repeated collection of reports done over time. For example, trends in users' private preferences or software usage may be monitored via such reports. We study the collection of such statistics in the local differential privacy (LDP) model, and describe an algorithm whose privacy cost is polylogarithmic in the number of changes to a user's value. More fundamentally---by building on anonymity of the users' reports---we also demonstrate how the privacy cost of our LDP algorithm can actually be much lower when viewed in the central model of differential privacy. We show, via a new and general privacy amplification technique, that any permutation-invariant algorithm satisfying $\varepsilon$-local differential privacy will satisfy $(O(\varepsilon \sqrt{\log(1/δ)/n}), δ)$-central differential privacy. By this, we explain how the high noise and $\sqrt{n}$ overhead of LDP protocols is a consequence of them being significantly more private in the central model. As a practical corollary, our results imply that several LDP-based industrial deployments may have much lower privacy cost than their advertised $\varepsilon$ would indicate---at least if reports are anonymized.

Nicolas Papernot, Shuang Song, Ilya Mironov et al.

The rapid adoption of machine learning has increased concerns about the privacy implications of machine learning models trained on sensitive data, such as medical records or other personal information. To address those concerns, one promising approach is Private Aggregation of Teacher Ensembles, or PATE, which transfers to a "student" model the knowledge of an ensemble of "teacher" models, with intuitive privacy provided by training teachers on disjoint data and strong privacy guaranteed by noisy aggregation of teachers' answers. However, PATE has so far been evaluated only on simple classification tasks like MNIST, leaving unclear its utility when applied to larger-scale learning tasks and real-world datasets. In this work, we show how PATE can scale to learning tasks with large numbers of output classes and uncurated, imbalanced training data with errors. For this, we introduce new noisy aggregation mechanisms for teacher ensembles that are more selective and add less noise, and prove their tighter differential-privacy guarantees. Our new mechanisms build on two insights: the chance of teacher consensus is increased by using more concentrated noise and, lacking consensus, no answer need be given to a student. The consensus answers used are more likely to be correct, offer better intuitive privacy, and incur lower-differential privacy cost. Our evaluation shows our mechanisms improve on the original PATE on all measures, and scale to larger tasks with both high utility and very strong privacy ($\varepsilon$ < 1.0).

Nicholas Carlini, Chang Liu, Úlfar Erlingsson et al.

This paper describes a testing methodology for quantitatively assessing the risk that rare or unique training-data sequences are unintentionally memorized by generative sequence models---a common type of machine-learning model. Because such models are sometimes trained on sensitive data (e.g., the text of users' private messages), this methodology can benefit privacy by allowing deep-learning practitioners to select means of training that minimize such memorization. In experiments, we show that unintended memorization is a persistent, hard-to-avoid issue that can have serious consequences. Specifically, for models trained without consideration of memorization, we describe new, efficient procedures that can extract unique, secret sequences, such as credit card numbers. We show that our testing strategy is a practical and easy-to-use first line of defense, e.g., by describing its application to quantitatively limit data exposure in Google's Smart Compose, a commercial text-completion neural network trained on millions of users' email messages.

Andrea Bittau, Úlfar Erlingsson, Petros Maniatis et al.

The large-scale monitoring of computer users' software activities has become commonplace, e.g., for application telemetry, error reporting, or demographic profiling. This paper describes a principled systems architecture---Encode, Shuffle, Analyze (ESA)---for performing such monitoring with high utility while also protecting user privacy. The ESA design, and its Prochlo implementation, are informed by our practical experiences with an existing, large deployment of privacy-preserving software monitoring. (cont.; see the paper)

Martín Abadi, Úlfar Erlingsson, Ian Goodfellow et al.

The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving privacy. However, older ideas about privacy may well remain valid and useful. This note reviews two recent works on privacy in the light of the wisdom of some of the early literature, in particular the principles distilled by Saltzer and Schroeder in the 1970s.

Nicolas Papernot, Martín Abadi, Úlfar Erlingsson et al.

Some machine learning applications involve training data that is sensitive, such as the medical histories of patients in a clinical trial. A model may inadvertently and implicitly store some of its training data; careful analysis of the model may therefore reveal sensitive information. To address this problem, we demonstrate a generally applicable approach to providing strong privacy guarantees for training data: Private Aggregation of Teacher Ensembles (PATE). The approach combines, in a black-box fashion, multiple models trained with disjoint datasets, such as records from different subsets of users. Because they rely directly on sensitive data, these models are not published, but instead used as "teachers" for a "student" model. The student learns to predict an output chosen by noisy voting among all of the teachers, and cannot directly access an individual teacher or the underlying data or parameters. The student's privacy properties can be understood both intuitively (since no single teacher and thus no single dataset dictates the student's training) and formally, in terms of differential privacy. These properties hold even if an adversary can not only query the student but also inspect its internal workings. Compared with previous work, the approach imposes only weak assumptions on how teachers are trained: it applies to any model, including non-convex models like DNNs. We achieve state-of-the-art privacy/utility trade-offs on MNIST and SVHN thanks to an improved privacy analysis and semi-supervised learning.

Úlfar Erlingsson

For computer software, our security models, policies, mechanisms, and means of assurance were primarily conceived and developed before the end of the 1970's. However, since that time, software has changed radically: it is thousands of times larger, comprises countless libraries, layers, and services, and is used for more purposes, in far more complex ways. It is worthwhile to revisit our core computer security concepts. For example, it is unclear whether the Principle of Least Privilege can help dictate security policy, when software is too complex for either its developers or its users to explain its intended behavior. This paper outlines a data-driven model for software security that takes an empirical, data-driven approach to modern software, and determines its exact, concrete behavior via comprehensive, online monitoring. Specifically, this paper briefly describes methods for efficient, detailed software monitoring, as well as methods for learning detailed software statistics while providing differential privacy for its users, and, finally, how machine learning methods can help discover users' expectations for intended software behavior, and thereby help set security policy. Those methods can be adopted in practice, even at very large scales, and demonstrate that data-driven software security models can provide real-world benefits.

Suman Jana, Úlfar Erlingsson, Iulia Ion

Clustering software into peer groups based on its apparent functionality allows for simple, intuitive categorization of software that can, in particular, help identify which software uses comparatively more privilege than is necessary to implement its functionality. Such relative comparison can improve the security of a software ecosystem in a number of ways. For example, it can allow market operators to incentivize software developers to adhere to the principle of least privilege, e.g., by encouraging users to use alternative, less-privileged applications for any desired functionality. This paper introduces software peer group analysis, a novel technique to identify least privilege violation and rank software based on the severity of the violation. We show that peer group analysis is an effective tool for detecting and estimating the severity of least privilege violation. It provides intuitive, meaningful results, even across different definitions of peer groups and security-relevant privileges. Our evaluation is based on empirically applying our analysis to over a million software items, in two different online software markets, and on a validation of our assumptions in a medium-scale user study.

Giulia Fanti, Vasyl Pihur, Úlfar Erlingsson

Techniques based on randomized response enable the collection of potentially sensitive data from clients in a privacy-preserving manner with strong local differential privacy guarantees. One of the latest such technologies, RAPPOR, allows the marginal frequencies of an arbitrary set of strings to be estimated via privacy-preserving crowdsourcing. However, this original estimation process requires a known set of possible strings; in practice, this dictionary can often be extremely large and sometimes completely unknown. In this paper, we propose a novel decoding algorithm for the RAPPOR mechanism that enables the estimation of "unknown unknowns," i.e., strings we do not even know we should be estimating. To enable learning without explicit knowledge of the dictionary, we develop methodology for estimating the joint distribution of two or more variables collected with RAPPOR. This is a critical step towards understanding relationships between multiple variables collected in a privacy-preserving manner.

Úlfar Erlingsson, Vasyl Pihur, Aleksandra Korolova

Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees. In short, RAPPORs allow the forest of client data to be studied, without permitting the possibility of looking at individual trees. By applying randomized response in a novel manner, RAPPOR provides the mechanisms for such collection as well as for efficient, high-utility analysis of the collected data. In particular, RAPPOR permits statistics to be collected on the population of client-side strings with strong privacy guarantees for each client, and without linkability of their reports. This paper describes and motivates RAPPOR, details its differential-privacy and utility guarantees, discusses its practical deployment and properties in the face of different attack models, and, finally, gives results of its application to both synthetic and real-world data.