Existing metadata-private messaging systems are either non-scalable or vulnerable to long-term traffic analysis. Approaches that mitigate traffic analysis attacks often suffer from unrealistic and unimplementable assumptions or impose system-wide bandwidth restrictions, degrading usability, and performance. In this work, we present a new model for metadata-private communication systems-deferred retrieval-that guarantees traffic analysis resistance under realistic, implementable user assumptions. We introduce Sparta systems, practical and scalable instantiations of deferred retrieval that are distributable, achieve high throughput, and support multiple concurrent conversations without message loss. Specifically, we present three Sparta constructions optimized for different scenarios: (i) low-latency, (ii) high-throughput in shared-memory environments (multi-thread implementations), and (iii) high throughput in shared-nothing (distributed) environments. Our low latency Sparta supports latencies of less than one millisecond, while our high-throughput Sparta can scale to deliver over 700,000 100B messages per second on a single 48-core server.

While disk encryption is suitable for use in most situations where confidentiality of disks is required, stronger guarantees are required in situations where adversaries may employ coercive tactics to gain access to cryptographic keys. Deniable volumes are one such solution in which the security goal is to prevent an adversary from discovering that there is an encrypted volume. Multiple snapshot attacks, where an adversary is able to gain access to two or more images of a disk, have often been proposed in the deniable storage system literature; however, there have been no concrete attacks proposed or carried out. We present the first multiple snapshot attack, and we find that it is applicable to most, if not all, implemented deniable storage systems. Our attack leverages the pattern of consecutive block changes an adversary would have access to with two snapshots, and demonstrate that with high probability it detects moderately sized and large hidden volumes, while maintaining a low false positive rate.