- General Chairs:
- Michael Brenner,
- Tancrède Lepoint,
- Kurt Rohloff
- Sponsor:
- sigsac
It is our great pleasure to welcome you to the 7th Workshop on Encrypted Computing & Applied Homomorphic Cryptography -- WAHC'19. This year's workshop continues its tradition of bringing together professionals, researchers and practitioners from academia, industry and government, to present, discuss and share the latest progress in the field of encrypted computing. Encrypted computing is a particular subfield of the area of computer security and applied cryptography, with an interest in practical applications of homomorphic encryption, multiparty computation, functional encryption, secure function evaluation, private information retrieval and searchable encryption.
The call for papers attracted submissions from Europe, America, and Asia. There were 16 submissions this year. The program committee was really active and produced 52 reviews. It was a challenging task to select 6 full papers and one demo to cover the wide range of techniques and interests of the community. We also encourage attendees to attend the keynote by Mariana Rakova. This valuable and insightful keynote will guide us to a better understanding of the practicality of advanced cryptography:
Proceeding Downloads
On the Feasibility and Impact of Standardising Sparse-secret LWE Parameter Sets for Homomorphic Encryption
In November 2018, the \urlHomomorphicEncryption.org consortium published the Homomorphic Encryption Security Standard. The Standard recommends several sets of Learning with Errors (LWE) parameters that can be selected by application developers to ...
Revisiting the Hybrid Attack on Sparse Secret LWE and Application to HE Parameters
In the practical use of the Learning With Error (LWE) based cryptosystems, it is quite common to choose the secret to be extremely small: one popular choice is ternary ((±1,0),coefficient vector, and some further use ternary vector having only small ...
Linear-Regression on Packed Encrypted Data in the Two-Server Model
Developing machine learning models from federated training data, containing many independent samples, is an important task that can significantly enhance the potential applicability and prediction power of learned models. Since single users, like ...
Zaphod: Efficiently Combining LSSS and Garbled Circuits in SCALE
We present modifications to the MPC system SCALE-MAMBA to enable the evaluation of garbled circuit (GC) based MPC functionalities and Linear Secret Sharing (LSSS) based MPC functionalities along side each other. This allows the user to switch between ...
nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data
In previous work, Boemer et al. introduced nGraph-HE, an extension to the Intel nGraph deep learning (DL) compiler, that enables data scientists to deploy models with popular frameworks such as TensorFlow and PyTorch with minimal code changes. However, ...
RAMPARTS: A Programmer-Friendly System for Building Homomorphic Encryption Applications
- David W. Archer,
- José Manuel Calderón Trilla,
- Jason Dagit,
- Alex Malozemoff,
- Yuriy Polyakov,
- Kurt Rohloff,
- Gerard Ryan
Homomorphic Encryption (HE) is an emerging technology that enables computing on data while the data is encrypted. A major challenge with homomorphic encryption is that it takes extensive expert knowledge to design meaningful and useful programs that are ...
- Proceedings of the 7th ACM Workshop on Encrypted Computing & Applied Homomorphic Cryptography
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Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| WAHC '18 | 17 | 6 | 35% |
| Overall | 17 | 6 | 35% |