
Explore secure stable matching algorithms for large-scale applications, focusing on privacy preservation and efficient computation in scenarios like medical residency matching.

Efficient framework for secure neural network inference combining homomorphic encryption and two-party computation, outperforming existing systems in speed while preserving privacy for cloud-based machine learning.

MAGE: An execution engine for secure computation, enabling efficient processing of large-scale encrypted data by leveraging oblivious memory access patterns to optimize virtual memory management.

Explores modifications to square-root ORAM for efficient random access in multi-party computation, offering significant performance improvements over existing approaches for various applications.

Explore cryptographic techniques for secure neural network computation, focusing on privacy-preserving methods and their applications in machine learning.

Explore high-throughput secure three-party computation with an honest majority in this presentation on advanced cryptographic protocols and their practical applications.

Explore secure computation with penalties, focusing on amortization techniques to enhance efficiency in cryptographic protocols for multi-party computations.

Explore advancements in secure computation techniques, focusing on enhanced privacy and efficiency in multi-party protocols presented at the ACM CCS 2016 conference.

Explore ObliVM, a framework for secure computation offering a domain-specific language, user-friendly abstractions, and efficient oblivious representations for various applications like data mining and genomic analysis.

Innovative distributed ORAM protocol for secure computation, offering significant bandwidth efficiency and performance improvements through novel vector dot product evaluation techniques.

Explore collusion-preserving computation, its definition, related work, criticism, security aspects, and applications in game theory.

Explore non-interactive secure multiparty computation, covering basic concepts, extensions, and applications. Learn about multi-input functional encryption and fully-robust NIMPC for various functions.

Learn to design fair protocols using Bitcoin, exploring concepts like fair exchange, claim-or-refund functionality, and secure computation with penalties in this cryptography-focused talk.

Explore leakage-tolerant computation techniques with input-independent preprocessing, focusing on multi-party computation models and innovative encoding methods for enhanced security.

Explore concurrent secure computation, focusing on optimal query complexity, circumventing impossibility results, and improving simulation techniques in cryptographic protocols.

Learn efficient techniques for secure multi-party computation, combining BMR and SPDZ approaches to achieve constant-round protocols with improved performance and security guarantees.

Explore advanced techniques for secure computation with minimal interaction, revisiting key concepts and presenting new insights in cryptography and data privacy.

Explore advanced techniques in multi-party computation, focusing on the transition from passive to active security through secure SIMD circuits.

Explore secure multi-party computation techniques for parallel RAM programs, focusing on large-scale applications and advanced cryptographic protocols.

Explore incoercible multi-party computation and its application in creating universally composable, receipt-free voting systems for enhanced election integrity.

Explore advanced cryptographic techniques for secure computation using leaky correlated randomness, enhancing privacy and security in distributed systems.