The increasing harms caused by hate, harassment, and other forms of abuse online have motivated major platforms to explore hierarchical governance. The idea is to allow communities to have designated members take on moderation and leadership duties; meanwhile, members can still escalate issues to the platform. But these promising approaches have only been explored in plaintext settings where community content is public to the platform. It is unclear how one can realize hierarchical governance in the huge and increasing number of online communities that utilize end-to-end encrypted (E2EE) messaging for privacy.We propose private hierarchical governance systems. These should enable similar levels of community governance as in plaintext settings, while maintaining cryptographic privacy of content and governance actions not reported to the platform. We design the first such system, taking a layered approach that adds governance logic on top of an encrypted messaging protocol; we show how an extension to the message layer security (MLS) protocol suffices for achieving a rich set of governance policies. Our approach allows developers to rapidly prototype new governance features, taking inspiration from a plaintext system called PolicyKit. We build a prototype E2EE messaging system called MlsGov that supports content-based community and platform moderation, elections of community moderators, votes to remove abusive users, and more.

We introduce the first practical protocols for fully decentralized sealed-bid auctions using timed commitments. Timed commitments ensure that the auction is finalized fairly even if all participants drop out after posting bids or if n bidders collude to try to learn the nth bidder's bid value. Our protocols rely on a novel non-malleable timed commitment scheme which efficiently supports range proofs to establish that bidders have sufficient funds to cover a hidden bid value. This allows us to penalize users who abandon bids for exactly the bid value, while supporting simultaneous bidding in multiple auctions with a shared collateral pool. Our protocols are concretely efficient and we have implemented them in an Ethereum-compatible smart contract which automatically enforces payment and delivery of an auctioned digital asset.

Verifiable registries allow clients to securely access a key-value mapping maintained by an untrusted server. Registries must be audited to ensure global invariants are preserved, which, in turn, allows for efficient monitoring of individual registry entries by their owners. To this end, existing proposals either assume trusted third-party auditors or rely on incrementally verifiable computation (IVC) via expensive recursive SNARKs to make registries client-auditable.

Private communication over the Internet remains a challenging problem. Even if messages are encrypted, it is hard to deliver them without revealing metadata about which pairs of users are communicating. Scalable anonymity systems, such as Tor, are susceptible to traffic analysis attacks that leak metadata. In contrast, the largest-scale systems with metadata privacy require passing all messages through a small number of providers, requiring a high operational cost for each provider and limiting their deployability in practice.

We initiate the systematic study of the energy complexity of algorithms (in addition to time and space complexity) based on Landauer's Principle in physics, which gives a lower bound on the amount of energy a system must dissipate if it destroys information. We propose energy-aware variations of three standard models of computation: circuit RAM, word RAM, and transdichotomous RAM. On top of these models, we build familiar high-level primitives such as control logic, memory allocation, and garbage collection with zero energy complexity and only constant-factor overheads in space and time complexity, enabling simple expression of energy-efficient algorithms. We analyze several classic algorithms in our models and develop low-energy variations: comparison sort, insertion sort, counting sort, breadth-first search, Bellman-Ford, Floyd-Warshall, matrix all-pairs shortest paths, AVL trees, binary heaps, and dynamic arrays. We explore the time/space/energy trade-off and develop several general techniques for analyzing algorithms and reducing their energy complexity. These results lay a theoretical foundation for a new field of semi-reversible computing and provide a new framework for the investigation of algorithms.