In response to calls for open data and growing privacy threats, organizations are increasingly adopting privacy-preserving techniques that add noise to published datasets. These techniques seek to protect privacy of data subjects while enabling useful analyses. With expert feedback, we developed empirically-driven documentation explaining the noise characteristics of two Wikipedia pageview datasets: one using rounding (heuristic privacy) and another using differential privacy (DP, formal privacy). We then used these documents to conduct a task-based contextual inquiry (n=15) exploring how data users—largely unfamiliar with these methods—perceive, interact with, and interpret privacy-preserving noise during data analysis.

In this work, we model the end-to-end pipeline of the advertising ecosystem, allowing us to identify two main issues with the current trajectory of private advertising proposals. First, prior work has largely considered ad targeting and engagement metrics individually rather than in composition. This has resulted in privacy notions that, while reasonable for each protocol in isolation, fail to compose to a natural notion of privacy for the ecosystem as a whole, permitting advertisers to extract new information about the audience of their advertisements. The second issue serves to explain the first: we prove that perfect privacy is impossible for any, even minimally, useful advertising ecosystem, due to the advertisers' expectation of conducting market research on the results. Having demonstrated that leakage is inherent in advertising, we re-examine what privacy could realistically mean in advertising, building on the well-established notion of sensitive data in a specific context. We identify that fundamentally new approaches are needed when designing privacy-preserving advertising subsystems in order to ensure that the privacy properties of the end-to-end advertising system are well aligned with people's privacy desires.

Differential privacy is a popular privacy-enhancing technology that has been deployed both by industry and government agencies. Unfortunately, existing explanations of differential privacy fail to set accurate privacy expectations for data subjects, which depend on the choice of deployment model. We design and evaluate new explanations of differential privacy for the local and central models, drawing inspiration from prior work explaining other privacy-enhancing technologies such as encryption. We reflect on the challenges in evaluating explanations and on the tradeoffs between qualitative and quantitative evaluation strategies. These reflections offer guidance for other researchers seeking to design and evaluate explanations of privacy-enhancing technologies.

Existing protocols for proving the correct execution of a RAM program in zero-knowledge are plagued by a processor expressiveness tradeoff: supporting fewer instructions results in smaller processor circuits (which improves performance), but may result in more program execution steps because non-supported instruction must be emulated over multiple processor steps (diminishing performance).

Widespread efforts to subvert access to strong cryptography has renewed interest in steganography, the practice of embedding sensitive messages in mundane cover messages. Recent efforts at provably secure steganography have focused on text-based generative models and cannot support other types of models, such as diffusion models, which are used for high-quality image synthesis. In this work, we study securely embedding steganographic messages into the output of image diffusion models. We identify that the use of variance noise during image generation provides a suitable steganographic channel. We develop our construction, Pulsar, by building optimizations to make this channel practical for communication. Our implementation of Pulsar is capable of embedding ≈320--613 bytes (on average) into a single image without altering the distribution of the generated image, all in < 3 seconds of online time on a laptop. In addition, we discuss how the results of Pulsar can inform future research into diffusion models. Pulsar shows that diffusion models are a promising medium for steganography and censorship resistance.

Smartphones form an important source of trust in modern computing. But, while their mobility is convenient, smartphones can be stolen or seized, allowing an adversary to impersonate the user in their digital life: accessing the user's services and decrypting their sensitive files. With this in mind, we build SocIoTy, which leverages a user's existing IoT devices to add a context-sensitive layer of security for non-expert users. Instead of assuming the existence of dedicated hardware, SocIoTy re-uses the devices of a user's smart home to provide cryptographic services, which we term at-home cryptography. We show that at-home cryptography can be built from simple cryptographic primitives, and that our SocIoTy solution is able to provide useful functionalities, like two-factor authentication (2FA) and secure file storage, while protecting against powerful adversaries in this setting. We implement and evaluate SocIoTy in real-world use cases and provide microbenchmarks for individual cryptographic operations on realistic models of IoT devices. We also provide full benchmarks of an end-to-end deployment on a simulated smart home, using a smartphone and 9 IoT devices to generate and display 2FA one-time passwords in less than 200 milliseconds. SocIoTy is able to provide strong, practical cryptography while binding its execution to the smart home itself, all without requiring additional hardware.

Multiparty garbling is the most popular approach for constant-round secure multiparty computation (MPC). Despite being the focus of significant research effort, instantiating prior approaches to multiparty garbling results in constant-round MPC that can not realistically accommodate large numbers of parties. In this work we present the first global-scale multiparty garbling protocol. The per-party communication complexity of our protocol decreases as the number of parties participating in the protocol increases - for the first time matching the asymptotic communication complexity of non-constant round MPC protocols. Our protocol achieves malicious security in the honest-majority setting and relies on the hardness of the Learning Party with Noise assumption.

We consider the problem of proving in zero-knowledge the existence of vulnerabilities in executables compiled to run on real-world processors. We demonstrate that it is practical to prove knowledge of real exploits for real-world processor architectures without the need for source code and without limiting our consideration to narrow vulnerability classes. To achieve this, we devise a novel circuit compiler and a toolchain that produces highly optimized, non-interactive zero-knowledge proofs for programs executed on the MSP430, an ISA commonly used in embedded hardware. Our toolchain employs a highly optimized circuit compiler and a number of novel optimizations to construct efficient proofs for program binaries. To demonstrate the capability of our system, we test our toolchain by constructing proofs for challenges in the Microcorruption capture the flag exercises.

Abstract Set membership proofs are an invaluable part of privacy preserving systems. These proofs allow a prover to demonstrate knowledge of a witness w corresponding to a secret element x of a public set, such that they jointly satisfy a given NP relation, i.e. ℛ(w, x) = 1 and x is a member of a public set {x 1, . . . , x𝓁}. This allows the identity of the prover to remain hidden, eg. ring signatures and confidential transactions in cryptocurrencies. In this work, we develop a new technique for efficiently adding logarithmic-sized set membership proofs to any MPC-in-the-head based zero-knowledge protocol (Ishai et al. [STOC’07]). We integrate our technique into an open source implementation of the state-of-the-art, post quantum secure zero-knowledge protocol of Katz et al. [CCS’18].We find that using our techniques to construct ring signatures results in signatures (based only on symmetric key primitives) that are between 5 and 10 times smaller than state-of-the-art techniques based on the same assumptions. We also show that our techniques can be used to efficiently construct post-quantum secure RingCT from only symmetric key primitives.

Despite recent widespread deployment of differential privacy, relatively little is known about what users think of differential privacy. In this work, we seek to explore users' privacy expectations related to differential privacy. Specifically, we investigate (1) whether users care about the protections afforded by differential privacy, and (2) whether they are therefore more willing to share their data with differentially private systems. Further, we attempt to understand (3) users' privacy expectations of the differentially private systems they may encounter in practice and (4) their willingness to share data in such systems. To answer these questions, we use a series of rigorously conducted surveys (n=2424).

Despite a long history of research and wide-spread applications to censorship resistant systems, practical steganographic systems capable of embedding messages into realistic communication distributions, like text, do not exist. We identify two primary impediments to deploying universal steganography: (1) prior work leaves the difficult problem of finding samplers for non-trivial distributions unaddressed, and (2) prior constructions have impractical minimum entropy requirements. We investigate using generative models as steganographic samplers, as they represent the best known technique for approximating human communication. Additionally, we study methods to overcome the entropy requirement, including evaluating existing techniques and designing a new steganographic protocol, called Meteor. The resulting protocols are provably indistinguishable from honest model output and represent an important step towards practical steganographic communication for mundane communication channels. We implement Meteor and evaluate it on multiple computation environments with multiple generative models.

Secure multiparty computation allows mutually distrusting parties to compute a function on their private inputs such that nothing but the function output is revealed. Achieving fairness --- that all parties learn the output or no one does -- is a long studied problem with known impossibility results in the standard model if a majority of parties are dishonest. We present a new model for achieving fairness in MPC against dishonest majority by using public bulletin boards implemented via existing infrastructure such as blockchains or Google's certificate transparency logs. We present both theoretical and practical constructions using either witness encryption or trusted hardware (such as Intel SGX). Unlike previous works that either penalize an aborting party or achieve weaker notions such as $\Delta$-fairness, we achieve complete fairness using existing infrastructure.