- Sponsor:
- sigsac
It is our great pleasure to welcome you to the 6th annual Workshop on Encrypted Computing and Applied Homomorphic Cryptography. WAHC was created in 2013 as a forum to organize and foster discussion of a wide variety of aspects of encrypted computing and secure computation.
In a world where distance is no longer an obstacle for cooperation, secure computation is becoming a key feature of current and future information systems. Distributed network applications and cloud architectures are at danger because lots of personal consumer data is aggregated in all kinds of formats and for various purposes. Industry and consumer electronics companies are facing massive threats like theft of intellectual property and industrial espionage. Public infrastructure has to be secured against sabotage and manipulation. A possible solution is encrypted computing: Data can be processed on remote, possibly insecure resources, while program code and data is encrypted all the time. This allows to outsource the computation of confidential information independently from the trustworthiness or the security level of the remote system. The goal of the workshop is to bring together researchers with practitioners and industry to present, discuss and to share the latest progress in the field. We want to exchange ideas that address real-world problems with practical approaches and solutions.
The workshop is typically uniformly attended by academia, government, and industry, with attendees both from prior years with experience in the domain and new attendees learning from the community. Specific encrypted computing technologies focus on homomorphic encryption and secure multiparty computation but also need to interoperate with existing technologies and infrastructures. The technologies and techniques discussed in this workshop are key to extending the range of applications that can be securely and practically outsourced.
Proceeding Downloads
Doing Real Work with FHE: The Case of Logistic Regression
We describe our recent experience, building a system that uses fully-homomorphic encryption (FHE) to approximate the coefficients of a logistic-regression model, built from genomic data. The aim of this project was to examine the feasibility of a ...
High-Throughput Secure AES Computation
This work describes a three-times ($3\times$) improvement to the performance of secure computation of AES over a network of three parties with an honest majority. The throughput that is achieved is even better than that of computing AES in some ...
More Practical Privacy-Preserving Machine Learning as A Service via Efficient Secure Matrix Multiplication
An efficient secure two-party computation protocol of matrix multiplication allows privacy-preserving cloud-aid machine learning services such as face recognition and traffic-aware navigation. We use homomorphic encryption to construct a secure matrix ...
Bit Decomposition Protocols in Secure Multiparty Computation
We present improved protocols for the conversion of secret-shared bit-vectors into secret-shared integers and vice versa, for the use as subroutines in secure multiparty computation (SMC) protocols and for protocols verifying the adherence of parties to ...
Marble: Making Fully Homomorphic Encryption Accessible to All
With the recent explosion of data breaches and data misuse cases, there is more demand than ever for secure system designs that fundamentally tackle today's data trust models. One promising alternative to today's trust model is true end-to-end ...
Implementation and Evaluation of Improved Gaussian Sampling for Lattice Trapdoors
We report on our implementation of a new Gaussian sampling algorithm for lattice trapdoors. Lattice trapdoors are used in a wide array of lattice-based cryptographic schemes including digital signatures, attributed-based encryption, program obfuscation ...
Index Terms
- Proceedings of the 6th 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% |