Vector processing has evolved from early systems like the CDC STAR-100 and Cray-1 to modern ISAs like ARM's Scalable Vector Extension (SVE) and RISC-V Vector (RVV) extensions.However, scaling vector processing for contemporary workloads presents challenges due to overheads in traditional architectures.We introduce Titan-I (T1), an out-of-order (OoO) RVV architecture designed

Oblivious RAM (ORAM) is an important cryptographic primitive that aims to protect against data access pattern leakage. With the recent theoretical improvements in ORAM protocols and the introduction of hardware-based trusted execution environments (TEEs), ORAM has become an increasingly practical design that starts to be adopted in real-world secure systems. In this paper, we study the bulk loading problem of ORAM, i.e., constructing an ORAM structure with a large amount of data, which can benefit many scenarios in secure cloud systems, such as data recovery, layout conversion, and query processing. We propose BULKOR, an extension of the state-of-the-art Path ORAM protocol. BULKOR supports the deployment with TEEs in untrusted servers, and satisfies the doubly-oblivious requirement to alleviate the side channel concerns in modern TEEs. BULKOR improves both the theoretical complexity from $\mathcal{O}\left( {N{{\log }^3}N} \right)$ to $\mathcal{O}\left( {N{{\log }^2}N} \right)$, and the practical performance of ORAM bulk loading, without sacrificing the security guarantees. It significantly outperforms the baseline designs Oblix and ZeroTrace by 8.7× to 54.6× and 5.8× to 533.1×, respectively, in various settings that implement ORAM on hard disks or in memory.

Cloud systems are now a prevalent platform to host large-scale big-data analytics applications such as machine learning and relational database. However, data privacy remains as a critical concern for public cloud systems. Existing trusted hardware could provide an isolated execution domain on an untrusted platform, but also suffers from access-pattern-based side channels at various levels including memory, disks, and networking. Oblivious algorithms can address these vulnerabilities by hiding the program data access patterns. Unfortunately, current oblivious algorithms for data analytics are limited to single-machine execution, only support simple operations, and/or suffer from significant performance overheads due to the use of expensive global sort and excessive data padding. In this work, we propose SODA, a set of efficient and oblivious algorithms for distributed data analytics operators, including filter, aggregate, and binary equi-join. To improve performance, SODA completely avoids the expensive oblivious global sort primitive, and minimizes the data padding overheads. SODA makes use of low-cost (pseudo-)random communication instead of expensive global sort to ensure uniform data traffic in oblivious filter and aggregate. It also adopts a novel two-level bin-packing approach in oblivious join to alleviate both input redistribution and join product skewness, thus minimizing necessary data padding. Compared to the state-of-the-art system, SODA not only extends the functionality but also improves the performance. It achieves 1.1× to 14.6× speedups on complex multi-operator data analytics workloads.