UC Santa Barbara

As computing continues to be used for increasingly private and sensitive operations impacting all aspects of our lives, the need to maintain tight control of those computations only continues to grow. This, when coupled with the increasing trend of “outsourced’' computation where datacenters are responsible for both storing data and performing computations over it on behalf of another party, naturally raises the level of importance of security and privacy even further. As such, algorithmic approaches to privacy-preserving and secure/trusted computations are rapidly emerging as a key aspect of workloads in datacenters at all scales. The higher cost associated with this additional algorithmic complexity will only increase the power consumption of these data centers, which are already receiving significant scrutiny for their ever more power-intensive operation. Architectural solutions are needed to support these emerging aspect of workloads. With the decline of Moore’s Law, this also presents an interesting prospect for several energy-efficient “Post-Moore” technologies such superconducting electronics and steep-slope devices which are studied and developed as potential replacements for Silicon-based CMOS to realize low power datacenter processors and accelerators.

In this dissertation, we study new opportunities for architectural support of these emerging application needs in both traditional and emerging technologies. To perform this work we need to make additional contributions advancing the modeling and evaluation of emerging Post-Moore technologies in the context of secure privacy-preserving computations. First, we show how using a small, co-located, trusted hardware device can be used to improve multiparty computation-based operations based on the trade-off of physical security and performance. Second, we show how near-data processing can be exploited to improve certain forms of homomorphic encryption with applications in private search. Finally, we explore how these emerging technologies can be used to improve energy-efficiency of datacenter workloads by modeling accelerators and multicore processors.