This paper addresses both of these observations. We give a hybrid algorithm that integrates lookahead into the state-based representation of an SMT solver and show that in the vast majority of cases it is possible to compute full lookahead up to depth four on inexpensive theories.
We develop a novel approach for paper bidding and assignment that is much more robust against such attacks. We show empirically that our approach provides robustness even when dishonest reviewers collude, have full knowledge of the assignment system’s internal workings, and have access to the system’s inputs.
This paper presents HashWires, a hash-based range proof protocol that is applicable in settings for which there is a trusted third party (typically a credential issuer) that can generate commitments. We refer to these as “credential-based” range proofs (CBRPs). HashWires improves upon hashchain solutions that are typically restricted to micro-payments for small interval ranges, achieving an exponential speedup in proof generation and verification time.
This paper presents Porcupine, an optimizing compiler that generates vectorized HE code using program synthesis. HE poses three major compilation challenges: it only supports a limited set of SIMD-like operators, it uses long-vector operands, and decryption can fail if ciphertext noise growth is not managed properly. Porcupine captures the underlying HE operator behavior so that it can automatically reason about the complex trade-offs imposed by these challenges to generate optimized, verified HE kernels.
We present a novel approach for blockchain asset owners to reclaim their funds in case of accidental private-key loss or transfer to a mistyped address. Our solution can be deployed upon failure or absence of proactively implemented backup mechanisms, such as secret sharing and cold storage.
This paper introduces Cheetah, a set of algorithmic and hardware optimizations for server-side HE DNN inference to approach real-time speeds. Cheetah proposes HE-parameter tuning optimization and operator scheduling optimizations, which together deliver 79× speedup over state-of-the-art.
To understand the effects of demographic attributes on attacker behavior in stolen social accounts, we devised a method to instrument and monitor such accounts. We then created, instrumented, and deployed more than 1000 Facebook accounts, and exposed them to criminals.
Secure multiparty computations enable the distribution of so-called shares of sensitive data to multiple parties such that the multiple parties can effectively process the data while being unable to glean much information about the data (at least not without collusion among all parties to put back together all the shares).