We show the security risk associated with using machine learning classifiers in United States election tabulators. The central classification task in election tabulation is deciding whether a mark does or does not appear on a bubble associated to an alternative in a contest on the ballot. Barretto et al. (E-Vote-ID 2021) reported that convolutional neural networks are a viable option in this field, as they outperform simple feature-based classifiers.

Despite decades of effort, a persistent chasm has existed between the theory and practice of device-level biometric authentication. Theoretical constructions can, in principle, provide biometric authentication with cryptographically secure public enrollment data. However, concrete implementations of these techniques have failed to provide security with real-world parameters. The result is that deployed authentication algorithms rely on data that overtly leaks private information about the biometric; thus systems rely on externalized security measures such as trusted execution environments.

This work introduces Private Eyes, the first zero-leakage biometric database. The only leakage of the system is unavoidable: 1) the log of the dataset size and 2) the fact that a query occurred. Private Eyes is built from oblivious symmetric searchable encryption. Approximate proximity queries are used: given a noisy reading of a biometric, the goal is to retrieve all stored records that are close enough according to a distance metric.

Risk-limiting audits (RLAs) are rigorous statistical procedures meant to detect invalid election results. RLAs examine paper ballots cast during the election to statistically assess the possibility of a disagreement between the winner determined by the ballots and the winner reported by tabulation. The design of an RLA must balance risk against efficiency: "risk" refers to a bound on the chance that the audit fails to detect such a disagreement when one occurs; "efficiency" refers to the total effort to conduct the audit.The most efficient approaches—when measured in terms of the number of ballots that must be inspected—proceed by "ballot comparison." However, ballot comparison requires an (untrusted) declaration of the contents of each cast ballot, rather than a simple tabulation of vote totals. This "cast-vote record table" (CVR) is then spot-checked against ballots for consistency. In many practical settings, the cost of generating a suitable CVR dominates the cost of conducting the audit which has prevented widespread adoption of these sample-efficient techniques.We introduce a new RLA procedure: an "adaptive ballot comparison" audit. In this audit, a global CVR is never produced; instead, a three-stage procedure is iterated: 1) a batch is selected, 2) a CVR is produced for that batch, and 3) a ballot within the batch is sampled, inspected by auditors, and compared with the CVR. We prove that such an audit can achieve risk commensurate with standard comparison audits while generating a fraction of the CVR. We present three main contributions: (1) a formal adversarial model for RLAs; (2) definition and analysis of an adaptive audit procedure with rigorous risk limits and an associated correctness analysis accounting for the incidental errors arising in typical audits; and (3) an analysis of efficiency.

Maintaining a resilient computer network is a delicate task with conflicting priorities. Flows should be served while controlling risk due to attackers. Upon publication of a vulnerability, administrators scramble to manually mitigate risk while waiting for a patch. We introduce$\textsc {Fashion}$: a linear optimizer that balances routing flows with the security risk posed by these flows.$\textsc {Fashion}$formalizes routing as a multi-commodity flow problem with side-constraints.$\textsc {Fashion}$formulates security using two approximations of risk in a probabilistic attack graph (Frigault et al. Network Security Metrics 2017).$\textsc {Fashion}$'s output is a set of software-defined networking rules consumable by Frenetic (Foster et al. ICFP 2011). We introduce a topology generation tool that creates data center network instances including flows and vulnerabilities.$\textsc {Fashion}$is executed on instances of up to 600 devices, thousands of flows, and million edge attack graphs. Solve time averages 30 minutes on the largest instances (seconds on the smallest instances). To ensure the security objective is accurate, the output solution is assessed using risk as defined by Frigault et al.$\textsc {Fashion}$allows enterprises to reconfigure their network in response to changes in functionality or security requirements.

Biometric databases collect people's information and allow users to perform proximity searches (finding all records within a bounded distance of the query point) with few cryptographic protections. This work studies proximity searchable encryption applied to the iris biometric.

Authentication systems are vulnerable to model inversion attacks where an adversary is able to approximate the inverse of a target machine learning model. Biometric models are a prime candidate for this type of attack. This is because inverting a biometric model allows the attacker to produce a realistic biometric input to spoof biometric authentication systems. One of the main constraints in conducting a successful model inversion attack is the amount of training data required. In this work, we focus on iris and facial biometric systems and propose a new technique that drastically reduces the amount of training data necessary. By leveraging the output of multiple models, we are able to conduct model inversion attacks with 1/10th the training set size of Ahmad and Fuller (IJCB 2020) for iris data and 1/1000th the training set size of Mai et al. (Pattern Analysis and Machine Intelligence 2019) for facial data. We denote our new attack technique as structured random with alignment loss.

Iris recognition systems transform an iris image into a feature vector. The seminal pipeline segments an image into iris and non-iris pixels, normalizes this region into a fixed-dimension rectangle, and extracts features which are stored and called a template (Daugman, 2009). This template is stored on a system. A future reading of an iris can be transformed and compared against template vectors to determine or verify the identity of an individual. As templates are often stored together, they are a valuable target to an attacker. We show how to invert templates across a variety of iris recognition systems. Our inversion is based on a convolutional neural network architecture we call RESIST (REconStructing IriSes from Templates). We apply RESIST to a traditional Gabor filter pipeline, to a DenseNet (Huang etal., CVPR 2017) feature extractor, and to a DenseNet architecture that works without normalization. Both DenseNet feature extractors are based on the recent ThirdEye recognition system (Ahmad and Fuller, BTAS 2019). When training and testing using the ND-0405 dataset, reconstructed images demonstrate a rank-1 accuracy of 100%, 76%, and 96% respectively for the three pipelines. The core of our approach is similar to an autoencoder. To obtain high accuracy this core is integrated into an adversarial network (Goodfellow et al., NeurIPS, 2014).

Fuzzy extractors (Dodis et al., SIAM J. Computing 2008) convert repeated noisy readings of a high-entropy secret into the same uniformly distributed key. A minimum condition for the security of the key is the hardness of guessing a value that is similar to the secret, because the fuzzy extractor converts such a guess to the key. We quantify this property in a new notion called fuzzy min-entropy. We ask: is fuzzy min-entropy sufficient to build fuzzy extractors? We provide two answers for different settings. 1) If the construction is provided a description of the probability distribution W that defines the noisy source then fuzzy min-entropy is a sufficient condition for information-theoretic key extraction from W . 2) A more ambitious goal is to design a single extractor that works for all possible sources. This more ambitious goal is impossible: there is a family of sources with high fuzzy min-entropy for which no single fuzzy extractor is secure. This is true in three settings: a) for standard fuzzy extractors, b) for fuzzy extractors that are allowed to sometimes be wrong, c) and for secure sketches, which are the main ingredient of most fuzzy extractor constructions.

Fuzzy extractors (Dodis et al., Eurocrypt 2004) convert repeated noisy readings of a high-entropy source into the same uniformly distributed key. The functionality of a fuzzy extractor outputs the key when provided with a value close to the original reading of the source. A necessary condition for security, called fuzzy min-entropy, is that the probability of every ball of values of the noisy source is small. Many noisy sources are best modeled using continuous metric spaces. To build continuous-source fuzzy extractors, prior work assumes that the system designer has a good model of the distribution (Verbitskiy et al., IEEE TIFS 2010). However, it is impossible to build an accurate model of a high entropy distribution just by sampling from the distribution. Model inaccuracy may be a serious problem. We demonstrate a family of continuous distributions W that is impossible to secure. No fuzzy extractor designed for W extracts a meaningful key from an average element of W. This impossibility result is despite the fact that each element W ∈ W has high fuzzy min-entropy. We show a qualitatively stronger negative result for secure sketches, which are used to construct most fuzzy extractors. Our results are for the Euclidean metric and are information-theoretic in nature. To the best of our knowledge all continuous-source fuzzy extractors argue information-theoretic security. Fuller, Reyzin, and Smith showed comparable negative results for a discrete metric space equipped with the Hamming metric (Asiacrypt 2016). Continuous Euclidean space necessitates new techniques.

Fuzzy extractors (Dodiset al., Eurocrypt 2004) turn a noisy secret into a stable, uniformly distributed key. Reusable fuzzy extractors remain secure when multiple keys are produced from a single noisy secret (Boyen, CCS 2004). Boyen showed information-theoretically secure reusable fuzzy extractors are subject to strong limitations. Simoens et al. (IEEE S&P, 2009) then showed deployed constructions suffer severe security breaks when reused. Canetti et al. (Eurocrypt 2016) used computational security to sidestep this problem, building a computationally secure reusable fuzzy extractor that corrects a sublinear fraction of errors.

Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator access and protects data in the case of system breaches. Since protected search was introduced in 2000, the area has grown rapidly, systems are offered by academia, start-ups, and established companies. However, there is no best protected search system or set of techniques. Design of such systems is a balancing act between security, functionality, performance, and usability. This challenge is made more difficult by ongoing database specialization, as some users will want the functionality of SQL, NoSQL, or NewSQL databases. This database evolution will continue, and the protected search community should be able to quickly provide functionality consistent with newly invented databases. At the same time, the community must accurately and clearly characterize the tradeoffs between different approaches. To address these challenges, we provide the following contributions:1) An identification of the important primitive operations across database paradigms. We find there are a small number of base operations that can be used and combined to support a large number of database paradigms.2) An evaluation of the current state of protected search systems in implementing these base operations. This evaluation describes the main approaches and tradeoffs for each base operation. Furthermore, it puts protected search in the context of unprotected search, identifying key gaps in functionality.3) An analysis of attacks against protected search for different base queries.4) A roadmap and tools for transforming a protected search system into a protected database, including an open-source performance evaluation platform and initial user opinions of protected search.

We have designed and implemented a general-purpose cryptographic building block, called GROK, for securing communication among groups of entities in networks composed of high-latency, low-bandwidth, intermittently connected links. During the process, we solved a number of non-trivial system problems. This paper describes these problems and our solutions, and motivates and justifies these solutions from three viewpoints: usability, efficiency, and security. The solutions described in this paper have been tempered by securing a widely-used group-oriented application, group text chat. We implemented a prototype extension to a popular text chat client called Pidgin and evaluated it in a real-world scenario. Based on our experiences, these solutions are useful to designers of group-oriented systems specifically, and secure systems in general.

Applications often re-transmit the same data, such as digital certificates, during repeated communication instances. Avoiding such superfluous transmissions with caching, while complicated, may be necessary in order to operate in low-bandwidth, high-latency wireless networks or in order to reduce communication load in shared, mobile networks. This paper presents a general framework and an accompanying software library, called "Authenticated Statement Exchange'' (ASE), for helping applications implement persistent caching of application-specific data. ASE supports secure caching of a number of pre-defined data types common to secure communication protocols and allows applications to define new data types to be handled by ASE. ASE is applicable to many applications. The paper describes the use of ASE in one such application, secure group chat. In a recent real-use deployment, ASE was instrumental in allowing secure group chat to operate over low-bandwidth satellite links.