Machine learning (ML) is increasingly being adopted in a wide variety of application
domains. Usually, a well-performing ML model relies on a large volume of training data and …

Software obfuscation has been developed for over 30 years. A problem always confusing
the communities is what security strength the technique can achieve. Nowadays, this …

Order-preserving encryption and its generalization order-revealing encryption (OPE/ORE)
allow sorting, performing range queries, and filtering data-all while only having access to …

Functional encryption (FE) is an extremely powerful cryptographic mechanism that lets an
authorized entity compute on encrypted data, and learn the results in the clear. However, all …

Emerging neural networks based machine learning techniques such as deep learning and
its variants have shown tremendous potential in many application domains. However, they …

Raziel combines secure multi-party computation and proof-carrying code to provide privacy,
correctness and verifiability guarantees for smart contracts on blockchains. Effectively …

Succinct non-interactive arguments (SNARGs) enable verifying NP computations with
substantially lower complexity than that required for classical NP verification. In this work, we …

Training complex neural network models using third-party cloud-based infrastructure among
multiple data sources is a promising approach among existing machine learning solutions …

In this work we investigate the problem of achieving secure computation by combining
stateless trusted devices with public ledgers. We consider a hybrid paradigm in which a …

Program obfuscation is a central primitive in cryptography, and has important real-world
applications in protecting software from IP theft. However, well known results from the …