Grant: Research Tools and Instruments

The digital economy is driven by (big) data. The ubiquitous collection of data by almost all stakeholders is exploited for economic benefit. For example, financial institutions may provide better services to their customers by the analysis of their credit card transactions and cell phone or online data. However that data may not only be used for social benefit, but also unwanted or even prohibited uses, e.g., violating one's privacy. Hence, it is necessary by law and for social acceptability to protect the privacy of individuals to the utmost possible extent and prevent unwanted inferences while still enabling the use of the data for the social or economic benefit. This challenge is exacerbated, since the data may be distributed among several entities each of whom has been entrusted with the data for a specific purpose. Cryptography can protect privacy during data storage, transmission, and processing, e.g. using homomorphic encryption or secure multi-party computation. In this proposal we aim to enable and optimize the efficiency of these techniques for the use in graphics processing units (GPUs). GPUs are often used to accelerate big data analysis, e.g. machine learning or inference. However, due to their use of floating-point arithmetic it is non-obvious how to adapt encryption technologies to GPUs. We aim to design and compare different software approaches to provide privacy-preserving processing of big data analyses in GPUs. To evaluate the efficiency impact of a modified software infrastructure, such as system libraries, and their interactive debugging of long-running programs, a dedicated hardware infrastructure consisting of several state-of-the-art GPUs is necessary. The research programs supported by this GPU infrastructure cover a large research team with a high degree of diversity and are done in close collaboration with industry partners. We plan to make all our software developed on the proposed infrastructure available as open-source. Furthermore, the cryptographic protocols and software libraries when deployed enable the privacy-preserving use of other computing infrastructures, such as Compute Canada or cloud computing services such as Amazon's AWS, for the processing of sensitive data, such as health-care data, personally identifiable information or trade secrets. Hence, the proposed infrastructure enhances the scalability and deployment options of the related industry research programs including to outsourced cloud infrastructures and provides the HQP with hands-on training in the IT skills -- security/privacy, cloud computing and data science -- with the highest demand in industry.