For those seeking end-to-end private communication free from pervasive metadata tracking and censorship, the Tor network has been the de-facto choice in practice, despite its susceptibility to traffic analysis attacks. Recently, numerous metadata-private messaging proposals have emerged with the aim to surpass Tor in the messaging context by obscuring the relationships between any two messaging buddies, even against global and active attackers. However, most of these systems face an undesirable usability constraint: they require a metadata-private “dialing” phase to establish mutual agreement and timing or round coordination before initiating any regular chats among users. This phase is not only resource-intensive but also inflexible, limiting users’ ability to manage multiple concurrent conversations seamlessly. For stringent privacy requirement, the often-enforced traffic uniformity further exacerbated the limitations of this roadblock. In this paper, we introduce PingPong, a new end-to-end system for metadata-private messaging designed to overcome these limitations. Under the same traffic uniformity requirement, PingPong replaces the rigid “dial-before-converse” paradigm with a more flexible “notify-before-retrieval” workflow. This workflow incorporates a metadata-private notification subsystem, PING, and a metadata-private message store, PONG. Both PING and PONG leverage hardware-assisted secure enclaves for performance and operates through a series of customized oblivious algorithms, while meeting the uniformity requirements for metadata protection. By allowing users to switch between conversations on demand, PingPong achieves a level of usability akin to modern instant messaging systems, while also offering improved performance and bandwidth utilization for goodput We have built a prototype of PingPong with 32 8-core servers equipped with enclaves and conducted a case study on a real-world messaging metadata dataset to validate our claims.

End-to-end encrypted messaging services (EEMSs) empower private communication through encrypting messages, yet also make content moderation for combating the spread of abusive messages challenging. There is an urgent call for supporting content moderation in EEMSs while ensuring user privacy. In this paper, we present a new system design for privacy-assured content moderation in EEMSs. At a high level, users in our system can privately report abusive messages, and the EEMS traces the source if a message has an aggregated report count exceeding a predefined threshold and is audited to be abusive. Our system mainly departs from prior works in that it allows flexible and adaptable thresholds, offers robustness against dishonest reporters providing malformed reports, and better ensures the privacy of all users during the moderation process. We also take a step further and propose a privacy-aware detection mechanism that relies on a blocklist built with transparency to mitigate the further spread of identified abusive messages from forwarders. Formal security analysis is provided and extensive experiments demonstrate the practical efficiency of our system.

With the increasing concern about data privacy and data explosion, some encrypted and compressed key-value (KV) stores have been proposed. A remarkable way to combine encryption and compression is to pack KV pairs into packs, and then compress and encrypt each pack separately. Recent research has shown that even if the data is encrypted, adversaries can still use the leaked information about data length and access frequency to launch pattern-analysis attacks. For this problem, some schemes have been proposed to protect the length and frequency distribution of packs. However, existing solutions protect such information at the cost of high storage and bandwidth overhead. In this paper, we propose an encrypted and compressed KV store with pattern-analysis security, which can resist pattern-analysis attacks with minimal overhead. We first devise a secure KV pair packing scheme, which guarantees pack length security with bounded storage overhead. Then we propose a$K$-indistinguishable pack frequency smoothing scheme. It can protect the distribution of pack frequency with minimal bandwidth overhead. We formally analyze the security of our design and implement our proposed secure KV storage system on Redis and RocksDB. Performance evaluation results demonstrate that our design minimizes the overhead of achieving pattern-analysis security.

Recent advances in cryptocurrencies have sparked significant interest in blockchain technology. However, scalability issues remain a major challenge for wide adoption of blockchains. Sharding is a promising approach to scale blockchains, but existing sharding-based blockchains fail to achieve expected performance gains due to limitations in cross-shard transaction processing. In this paper, we propose X-shard, a blockchain system that optimizes cross-shard transaction processing, achieving high effective throughput and low processing latency. First, we allocate transactions to shards based on historical transaction patterns to minimize cross-shard transactions. Second, we take an optimistic strategy to process cross-shard transactions in parallel as sub-transactions within input shards, thereby accelerating transaction processing. Finally, we employ a cross-shard commit protocol with threshold signatures to reduce communication overhead. We implement and deploy X-shard on Amazon EC2 clusters. Experimental results validate our theoretical analysis and show that as the number of shards increases, X-shard achieves nearly linear scaling in effective throughput and decreases in transaction processing latency.