Algorithmic Primitives for Quantum-Assisted Quantum Control

We discuss two primitive algorithms to evaluate overlaps and transition matrix time series, which are used to construct a variety of quantum-assisted quantum control algorithms implementable on NISQ devices. Unlike previous approaches, our method bypasses tomographically complete measurements and instead relies solely on single qubit measurements. We analyse circuit complexity of composed algorithms and sources of noise arising from Trotterization and measurement errors.Comment: 9 pages, comments welcom

Group Structure in Correlations and Its Applications in Cryptography

Correlated random variables are a key tool in cryptographic applications like secure multi-party computation. We investigate the power of a class of correlations that we term group correlations: A group correlation is a uniform distribution over pairs $(x,y) \in G^2$ such that $x+y\in S$, where $G$ is a (possibly non-abelian) group and $S$ is a subset of $G$. We also introduce bi-affine correlations and show how they relate to group correlations. We present several structural results, new protocols, and applications of these correlations. The new applications include a completeness result for black-box group computation, perfectly secure protocols for evaluating a broad class of black box ``mixed-groups\u27\u27 circuits with bi-affine homomorphism, and new information-theoretic results. Finally, we uncover a striking structure underlying OLE: In particular, we show that OLE over $\mathrm{GF}(2^n)$, is isomorphic to a group correlation over $\mathbb{Z}_4^n$

End-to-End Secure Messaging with Traceability Only for Illegal Content

As end-to-end encrypted messaging services become widely adopted, law enforcement agencies have increasingly expressed concern that such services interfere with their ability to maintain public safety. Indeed, there is a direct tension between preserving user privacy and enabling content moderation on these platforms. Recent research has begun to address this tension, proposing systems that purport to strike a balance between the privacy of \u27\u27honest\u27\u27 users and traceability of \u27\u27malicious\u27\u27 users. Unfortunately, these systems suffer from a lack of protection against malicious or coerced service providers. In this work, we address the privacy vs. content moderation question through the lens of pre-constrained cryptography [Ananth et al., ITCS 2022]. We introduce the notion of set pre-constrained (SPC) group signatures that guarantees security against malicious key generators. SPC group signatures offer the ability to trace users in messaging systems who originate pre-defined illegal content (such as child sexual abuse material), while providing security against malicious service providers. We construct concretely efficient protocols for SPC group signatures, and demonstrate the real-world feasibility of our approach via an implementation. The starting point for our solution is the recently introduced Apple PSI system, which we significantly modify to improve security and expand functionality

ZEBRA: SNARK-based Anonymous Credentials for Practical, Private and Accountable On-chain Access Control

Restricting access to certified users is not only desirable for many blockchain applications, it is also legally mandated for decentralized finance (DeFi) applications to counter malicious actors. Existing solutions, however, are either (i) non-private, i.e., they reveal the link between users and their wallets to the authority granting credentials, or (ii) they introduce additional trust assumptions by relying on a decentralized oracle to verify anonymous credentials (ACs). To remove additional trust in the latter approach, we propose verifying credentials on-chain in this work. We find that this approach has impractical costs with prior AC schemes, and propose a new AC scheme ZEBRA that crucially relies on zkSNARKs to provide efficient on-chain verification for the first time. In addition to the standard unlinkability property that provides privacy for users, ZEBRA also supports auditability, revocation, traceability, and theft detection, which adds accountability for malicious users and convenience for honest users to our access control solution. Even with these properties, ZEBRA reduces the gas cost incurred on the Ethereum Virtual Machine (EVM) by 14.3x when compared to Coconut [NDSS 2019], the state-of-the-art AC scheme for blockchains that only provides unlinkability. This improvement translates to a reduction in transaction fees from 176 USD to 12 USD on Ethereum in May 2023. Since 12 USD is still high for most applications, ZEBRA further drives down credential verification costs through batched verification. For a batch of 512 layer-1 and layer-2 wallets, the transaction fee on Ethereum is reduced to just 0.44 USD and 0.02 USD, respectively, which is comparable to the minimum transaction costs on Ethereum

Post-Quantum Privacy Pass via Post-Quantum Anonymous Credentials

It is known that one can generically construct a post-quantum anonymous credential scheme, supporting the showing of arbitrary predicates on its attributes using general-purpose zero-knowledge proofs secure against quantum adversaries [Fischlin, CRYPTO 2006]. Traditionally, such a generic instantiation is thought to come with impractical sizes and performance. We show that with careful choices and optimizations, such a scheme can perform surprisingly well. In fact, it performs competitively against state-of-the-art post-quantum blind signatures, for the simpler problem of post-quantum unlinkable tokens, required for a post-quantum version of Privacy Pass. To wit, a post-quantum Privacy Pass constructed in this way using zkDilithium, our proposal for a STARK-friendly variation on Dilithium2, allows for a trade-off between token size (85–175KB) and generation time (0.3–5s) with a proof security level of 115 bits. Verification of these tokens can be done in 20–30ms. We argue that these tokens are reasonably practical, adding less than a second upload time over traditional tokens, supported by a measurement study. Finally, we point out a clear advantage of our approach: the flexibility afforded by the general purpose zero-knowledge proofs. We demonstrate this by showing how we can construct a rate-limited variant of Privacy Pass that doesn\u27t not rely on non-collusion for privacy

Experimenting with Zero-Knowledge Proofs of Training

How can a model owner prove they trained their model according to the correct specification? More importantly, how can they do so while preserving the privacy of the underlying dataset and the final model? We study this problem and formulate the notion of zero-knowledge proof of training (zkPoT), which formalizes rigorous security guarantees that should be achieved by a privacy-preserving proof of training. While it is theoretically possible to design zkPoT for any model using generic zero-knowledge proof systems, this approach results in extremely unpractical proof generation times. Towards designing a practical solution, we propose the idea of combining techniques from MPC-in-the-head and zkSNARKs literature to strike an appropriate trade-off between proof size and proof computation time. We instantiate this idea and propose a concretely efficient, novel zkPoT protocol for logistic regression. Crucially, our protocol is streaming-friendly and does not require RAM proportional to the size of the circuit being trained and, hence, can be adapted to the requirements of available hardware. We expect the techniques developed in this paper to also generally be useful for designing efficient zkPoT protocols for other relatively more sophisticated ML models. We implemented and benchmarked prover/verifier runtimes and proof sizes for training a logistic regression model using mini-batch gradient descent on a 4~GB dataset of 262,144 records with 1024 features. We divide our protocol into three phases: (1) data-independent offline phase (2) data-dependent phase that is independent of the model (3) online phase that depends both on the data and the model. The total proof size (across all three phases) is less than $10\%$ of the data set size ($<350$~MB). In the online phase, the prover and verifier times are under 10 minutes and half a minute respectively, whereas in the data-dependent phase, they are close to one hour and a few seconds respectively