In a world where financial transactions are primarily performed or recorded online, protecting sensitive transaction details has become crucial. Roommates sharing housing costs or friends splitting travelling expenses may use applications such as Splitwise to easily track debts and minimize the number of individual repayments. However, these apps reveal potentially sensitive financial transaction activity to their operators. In this paper, we present Silent Splitter, a privacy-preserving payment splitting system which enables users to securely set up groups, perform transactions within those groups, and ``settle up'' without revealing group membership or any sensitive transaction details (such as the users involved or amount of money exchanged) to the system itself. Silent Splitter operates in the two server setting and uses Distributed Point Functions (DPFs) to securely record transactions. Of independent interest, we also present new protocols for proving knowledge of properties of DPFs as part of our system.

Private messaging platforms provide strong protection against platform eavesdropping, but malicious users can use privacy as cover for spreading abuse and misinformation. In an attempt to identify the sources of misinformation on private platforms, researchers have proposed mechanisms to trace back the source of a user-reported message (CCS '19,'21). Unfortunately, the threat model considered by initial proposals allowed a single user to compromise the privacy of another user whose legitimate content the reporting user did not like. More recent work has attempted to mitigate this side effect by requiring a threshold number of users to report a message before its origins can be identified (NDSS '22). However, the state of the art scheme requires the introduction of new probabilistic data structures and only achieves a "fuzzy" threshold guarantee. Moreover, false positives, where the source of an unreported message is identified, are possible. This paper introduces a new threshold source tracking technique that allows a private messaging platform, with the cooperation of a third-party moderator, to operate a threshold reporting scheme with exact thresholds and no false positives. Unlike prior work, our techniques require no modification of the message delivery process for a standard source tracking scheme, affecting only the abuse reporting procedure, and do not require tuning of probabilistic data structures.

To resist the regimes of ubiquitous surveillance imposed upon us in every facet of modern life, we need technological tools that subvert surveillance systems. Unfortunately, while cryptographic tools frequently demonstrate how we can construct systems that safeguard user privacy, there is limited motivation for corporate entities engaged in surveillance to adopt these tools, as they often clash with profit incentives. This paper demonstrates how, in one particular aspect of everyday life -- customer loyalty programs -- users can subvert surveillance and attain anonymity, without necessitating any cooperation or modification in the behavior of their surveillors. We present the CheckOut system, which allows users to coordinate large anonymity sets of shoppers to hide the identity and purchasing habits of each particular user in the crowd. CheckOut scales up and systematizes past efforts to subvert loyalty surveillance, which have been primarily ad-hoc and manual affairs where customers physically swap loyalty cards to mask their real identities. CheckOut allows increased scale while ensuring that the necessary computing infrastructure does not itself become a new centralized point of privacy failure. Of particular importance to our scheme is a protocol for loyalty programs that offer reward points, where we demonstrate how CheckOut can assist users in paying each other back for loyalty points accrued while using each others' loyalty accounts. We present two different mechanisms to facilitate redistributing rewards points, offering trade-offs in functionality, performance, and security.

We introduce new compression side-channel attacks against database storage engines that simultaneously support compression of database pages and encryption at rest. Given only limited, indirect access to an encrypted and compressed database table, our attacks extract arbitrary plaintext with high accuracy. We demonstrate accurate and performant attacks on the InnoDB storage engine variants found in MariaDB and MySQL as well as the WiredTiger storage engine for MongoDB.Our attacks overcome obstacles unique to the database setting that render previous techniques developed to attack TLS ineffective. Unlike the web setting, where the exact length of a compressed and encrypted message can be observed, we make use of only approximate ciphertext size information gleaned from file sizes on disk. We amplify this noisy signal and combine it with new attack heuristics tailored to the database setting to extract secret plaintext. Our attacks can detect whether a random string appears in a table with > 90% accuracy and extract 10-character random strings from encrypted tables with > 95% success.

While the end-to-end encryption properties of popular messaging schemes such as Whatsapp, Messenger, and Signal guarantee privacy for users, these properties also make it very difficult for messaging platforms to enforce any sort of content moderation. This can lead to the unchecked spread of malicious content such as misinformation on such platforms. In 2019, Tyagi et al. developed message traceback, which addresses this issue by allowing a messaging platform to recover the path of a forwarded message after a user reports it for malicious content. This paper presents an alternative to message traceback that offers more privacy to users and requires less platform-side storage. We term this approach source-tracking for encrypted messaging schemes. Source-tracking enables messaging platforms to provide the privacy guarantees expected from standard end-to-end encryption, but also helps hold the sources of malicious messages accountable: if malicious content is reported by a user, the source can be identified. We formalize security goals for source-tracking schemes and design and implement two source-tracking schemes with different security and performance tradeoffs.

In a Single Secret Leader Election (SSLE), a group of participants aim to randomly choose exactly one leader from the group with the restriction that the identity of the leader will be known to the chosen leader and nobody else. At a later time, the elected leader should be able to publicly reveal her identity and prove that she has won the election. The election process itself should work properly even if many registered users are passive and do not send any messages. Among the many applications of SSLEs, their potential for enabling more efficient proof-of-stake based cryptocurrencies have recently received increased attention.

Widely used payment splitting apps allow members of a group to keep track of debts between members by sending charges for expenses paid by one member on behalf of others. While offering a great deal of convenience, these apps gain access to sensitive data on users’ financial transactions. In this paper, we present a payment splitting app that hides all transaction data within a group from the service provider, provides privacy protections between users in a group, and provides integrity against malicious users or even a malicious server.

Users regularly enter sensitive data, such as passwords, credit card numbers, or tax information, into the browser window. While modern browsers provide powerful client-side privacy measures to protect this data, none of these defenses prevent a browser compromised by malware from stealing it. In this work, we present Fidelius, a new architecture that uses trusted hardware enclaves integrated into the browser to enable protection of user secrets during web browsing sessions, even if the entire underlying browser and OS are fully controlled by a malicious attacker. Fidelius solves many challenges involved in providing protection for browsers in a fully malicious environment, offering support for integrity and privacy for form data, JavaScript execution, XMLHttpRequests, and protected web storage, while minimizing the TCB. Moreover, interactions between the enclave and the browser, the keyboard, and the display all require new protocols, each with their own security considerations. Finally, Fidelius takes into account UI considerations to ensure a consistent and simple interface for both developers and users. As part of this project, we develop the first open source system that provides a trusted path from input and output peripherals to a hardware enclave with no reliance on additional hypervisor security assumptions. These components may be of independent interest and useful to future projects. We implement and evaluate Fidelius to measure its performance overhead, finding that Fidelius imposes acceptable overhead on page load and user interaction for secured pages and has no impact on pages and page components that do not use its enhanced security features.

Hardware enclaves such as Intel SGX are a promising technology for improving the security of databases outsourced to the cloud. These enclaves provide an execution environment isolated from the hyper-visor/OS, and encrypt data in RAM. However, for applications that use large amounts of memory, including most databases, enclaves do not protect against access pattern leaks, which let attackers gain a large amount of information about the data. Moreover, the naïve way to address this issue, using Oblivious RAM (ORAM) primitives from the security literature, adds substantial overhead. A number of recent works explore trusted hardware enclaves as a path toward secure, access-pattern oblivious outsourcing of data storage and analysis. While these works efficiently solve specific subproblems (e.g. building secure indexes or running analytics queries that always scan entire tables), no prior work has supported oblivious query processing for general query workloads on a DBMS engine with multiple access methods. Moreover, applying these techniques individually does not guarantee that an end-to-end workload, such as a complex SQL query over multiple tables, will be oblivious. In this paper, we introduce ObliDB, an oblivious database engine design that is the first system to provide obliviousness for general database read workloads over multiple access methods. ObliDB introduces a diverse array of new oblivious physical operators to accelerate oblivious SQL queries, giving speedups of up to an order of magnitude over naïve ORAM. It supports a broad range of queries, including aggregation, joins, insertions, deletions and point queries. We implement ObliDB and show that, on analytics workloads, ObliDB ranges from 1.1--19x faster than Opaque, a previous oblivious, enclave-based system designed only for analytics, and comes within 2.6 x of Spark SQL, which provides no security guarantees. In addition, ObliDB supports point queries with 3--10ms latency, which is comparable to index-only trusted hardware systems, and runs over 7x faster than HIRB, a previous encryption-based oblivious index system that supports point queries.

Abstract Certificate transparency (CT) is an elegant mechanism designed to detect when a certificate authority (CA) has issued a certificate incorrectly. Many CAs now support CT and it is being actively deployed in browsers. However, a number of privacy-related challenges remain. In this paper we propose practical solutions to two issues. First, we develop a mechanism that enables web browsers to audit a CT log without violating user privacy. Second, we extend CT to support non-public subdomains.