Efficient Isogeny Proofs Using Generic Techniques

Generating supersingular elliptic curves of unknown endomorphism ring has been a problem vexing isogeny-based cryptographers for several years. A recent development has proposed a trusted setup protocol to generate such a curve, where each participant generates and proves knowledge of an isogeny. Thus, the construction of efficient proofs of knowledge of isogeny has developed new interest. Historically, the isogeny community has assumed that obtaining isogeny proofs of knowledge from generic proof systems, such as zkSNARKs, was not a practical approach. We contribute the first concrete result in this area by applying Aurora (EUROCRYPT\u2719), Ligero (CCS\u2717) and Limbo (CCS\u2721) to an isogeny path relation, and comparing their performance to a state-of-the-art, tailor-made protocol for the same relation. In doing so, we show that modern generic proof systems are competitive when applied to isogeny assumptions, and provide an order of magnitude ($3\textrm{-}10\times$) improvement to proof and verification times, with similar proof sizes. In addition, these proofs provide a stronger notion of soundness, and statistical zero-knowledge; a property that has only recently been achieved in isogeny PoKs. Independently, this technique shows promise as a component in the design of future isogeny-based or other post-quantum protocols

Optimizing Registration Based Encryption

The recent work of Garg et al. from TCC\u2718 introduced the notion of registration based encryption (RBE). The principal motivation behind RBE is to remove the key escrow problem of identity based encryption (IBE), where the IBE authority is trusted to generate private keys for all the users in the system. Although RBE has excellent asymptotic properties, it is currently impractical. In our estimate, ciphertext size would be about 11 terabytes in an RBE deployment supporting 2 billion users. Motivated by this observation, our work attempts to reduce the concrete communication and computation cost of the current state-of-the-art construction. Our contribution is two-fold. First, we replace Merkle trees with crit-bit trees, a form of PATRICIA trie, without relaxing any of the original RBE efficiency requirements introduced by Garg et al. This change reduces the ciphertext size by 15% and the computation cost of decryption by 30%. Second, we observe that increasing RBE\u27s public parameters by a few hundred kilobytes could reduce the ciphertext size by an additional 50%. Overall, our work decreases the ciphertext size by 57.5%

Panacea: Non-interactive and Stateless Oblivious RAM

Oblivious RAM (ORAM) allows a client to outsource storage to a remote server while hiding the data access pattern from the server. Many ORAM designs have been proposed to reduce the computational overhead and bandwidth blowup for the client. A recent work, Onion Ring ORAM (CCS\u2719), is able to achieve $O(1)$ bandwidth blowup in the online phase using fully homomorphic encryption (FHE) techniques, at the cost of a computationally expensive client-side offline phase. Furthermore, such a scheme can be categorized as a stateful construction, meaning that the client has to locally maintain a dynamic state representing the order of remote database elements. We present Panacea: a novel design of ORAM based on FHE techniques, that is non-interactive and stateless, achieves $O(1)$ bandwidth blowup, and does not require an expensive offline phase for the client to perform; in that sense, our design is the first of its kind among other ORAM designs. To provide the client with such performance benefits, our design delegates all expensive computation to the resourceful server. We additionally show how to boost the server performance significantly using probabilistic batch codes at the cost of only 1.5x in additional bandwidth blowup and 3x expansion in server storage, but less amortized bandwidth. Our experimental results show that our design, with the batching technique, is practical in terms of server computation overhead as well. Specifically, for a database size of $2^{19}$, it takes only $1.16$ seconds of amortized computation time for a server to respond to a query. As a result of the statelessness and low computational overhead on the client, and reasonable computational overhead on the server, our design is very suitable to be deployed as a cloud-based privacy-preserving storage outsourcing solution with a portable client running on a lightweight device

Efficient and Secure kk-NN Classification from Improved Data-Oblivious Programs and Homomorphic Encryption

The $k$-nearest neighbors classifier is a simple machine learning algorithm with applications in image recognition, finance, medical diagnosis and so on. It involves a measurement which is compared against a database of preclassified vectors, so that the result depends on the $k$ vectors in the database that are closest to the measurement. In the client-server model, this classification process can be outsourced to an external party that offers machine learning as a service, where the measurement is sent in the form of a query. However, this raises privacy concerns if sensitive information is contained in the query. We design a secure and non-interactive version of the $k$-nearest neighbors classifier, based on fully homomorphic encryption, which does not leak any information about the query to the server. Our algorithm is instantiated with the TFHE homomorphic encryption scheme, and the selection of the top-$k$ elements is done with a novel strategy based on a type of data-oblivious algorithm---sorting networks. Compared to prior work from PoPETs 2021, the asymptotic complexity is improved from $O(d^2)$ to $O(d \log^2 {k})$, where $d$ is the number of entries in the $k$-NN model. Experimental results show that the proposed protocol can be up to 16 times faster (not accounting for difference in CPU) than previous approaches for a moderately sized database

Implicit Consensus: Blockchain with Unbounded Throughput

Recently, the blockchain technique was put in the spotlight as it introduced a systematic approach for multiple parties to reach consensus without needing trust. However, the application of this technique in practice is severely restricted due to its limitations in throughput. In this paper, we propose a novel consensus model, namely the implicit consensus, with a distinctive blockchain-based distributed ledger in which each node holds its individual blockchain. In our system, the consensus is not on the transactions, but on a special type of blocks called Check Points that are used to validate individual transactions. Our system exploits the ideas of self-interest and spontaneous sharding and achieves unbounded throughput with the transaction reliability that equivalent to traditional Byzantine fault tolerance schemes

Improved All-but-One Vector Commitment with Applications to Post-Quantum Signatures

Post-quantum digital signature schemes have recently received increased attention due to the NIST standardization project for additional signatures. MPC-in-the-Head and VOLE-in-the-Head are general techniques for constructing such signatures from zero-knowledge proof systems. A common theme between the two is an all-but-one vector commitment scheme which internally uses GGM trees. This primitive is responsible for a significant part of the computational time during signing and verification. A more efficient technique for constructing GGM trees is the half-tree technique, introduced by Guo et al. (Eurocrypt 2023). Our work builds an all-but-one vector commitment scheme from the half-tree technique, and further generalizes it to an all-but-$\tau$ vector commitment scheme. Crucially, our work avoids the use of the random oracle assumption in an important step, which means our binding proof is non-trivial and instead relies on the random permutation oracle. Since this oracle can be instantiated using fixed-key AES which has hardware support, we achieve faster signing and verification times. We integrate our vector commitment scheme into FAEST (faest.info), a round one candidate in the NIST standardization process, and demonstrates its performance with a prototype implementation. For $\lambda = 128$, our experimental results show a nearly $3.5$-fold improvement in signing and verification times

Reusable, Instant and Private Payment Guarantees for Cryptocurrencies

Despite offering numerous advantages, public decentralized cryptocurrencies such as Bitcoin suffer from scalability issues such as high transaction latency and low throughput. The vast array of so-called Layer-2 solutions tackling the scalability problem focus on throughput, and consider latency as a secondary objective. However, in the context of retail payments, instant finality of transactions is arguably a more pressing concern, besides the overarching concern for privacy. In this paper, we provide an overlay network that allows privacy-friendly low latency payments in a retail market. Our approach follows that of a recent work called Snappy, which achieved low latency but exposed identities of customers and their transaction histories. Our construction ensures this data is kept private, while providing merchants with protection against double-spending attacks. Although our system is still based upon customers registering with a collateral, crucially this collateral is reusable over time. The technical novelty of our work comes from randomness-reusable threshold encryption (RRTE), a cryptographic primitive we designed specifically for the following features: our construction provably guarantees payments to merchants, preserves the secret identity of honest customers and prevents their transactions from being linked. We also present an implementation of our construction, showing its capacity for fast global payments in a retail setting with a delay of less than 1 second

The Key Lattice Framework for Concurrent Group Messaging

Today, two-party secure messaging is well-understood and widely adopted on the Internet, e.g., Signal and WhatsApp. Multiparty protocols for secure group messaging on the other hand are less mature and many protocols with different tradeoffs exist. Generally, such protocols require parties to first agree on a shared secret group key and then periodically update it while preserving forward secrecy (FS) and post compromise security (PCS). We present a new framework, called a key lattice, for managing keys in concurrent group messaging. Our framework can be seen as a ``key management\u27\u27 layer that enables concurrent group messaging when secure pairwise channels are available. Proving security of group messaging protocols using the key lattice requires new game-based security definitions for both FS and PCS. Our new definitions are both simpler and more natural than previous ones, as our framework combines both FS and PCS into directional variants of the same abstraction, and additionally avoids dependence on time-based epochs. Additionally, we give a concrete, standalone instantiation of a concurrent group messaging protocol for dynamic groups. Our protocol provides both FS and PCS, supports concurrent updates, and only incurs $O(1)$ overhead for securing the messaging payload, $O(n)$ update cost and $O(n)$ healing costs, which are optimal

SortingHat: Efficient Private Decision Tree Evaluation via Homomorphic Encryption and Transciphering

Machine learning as a service scenario typically requires the client to trust the server and provide sensitive data in plaintext. However, with the recent improvements in fully homomorphic encryption (FHE) schemes, many such applications can be designed in a privacy preserving way. In this work, we focus on such a problem, private decision tree evaluation (PDTE) --- where a server has a decision tree classification model, and a client wants to use the model to classify her private data without revealing the data or the classification result to the server. We present an efficient non-interactive design of PDTE, that we call SortingHat, based on FHE techniques. As part of our design, we solve multiple cryptographic problems related to FHE: (1) we propose a fast homomorphic comparison function where one input can be in plaintext format; (2) we design an efficient binary decision tree evaluation technique in the FHE setting, which we call homomorphic traversal, and apply it together with our homomorphic comparison to evaluate private decision tree classifiers, obtaining running times orders of magnitude faster than the state of the art; (3) we improve both the communication cost and the time complexity of transciphering, by applying our homomorphic comparison to the FiLIP stream cipher. Through a prototype implementation, we demonstrate that our improved transciphering solution runs around 400 times faster than previous works. We finally present a choice in terms of PDTE design: we present a version of SortingHat without transciphering that achieves significant improvement in terms of computation cost comparing to prior works; and another version with transciphering that has a communication cost about 20 thousand times smaller but comparable running time