arXiv subjects

Zirui Neil Zhao

Publications and source records attributed to Zirui Neil Zhao.

4 recordsLinked to original sources

arXiv2026

Onyx: Cost-Efficient Disk-Oblivious ANN Search

Approximate nearest neighbor (ANN) search in AI systems increasingly handles sensitive data on third-party infrastructure. Trusted execution environments (TEEs) offer protection, but cost-efficient deployments must rely on external SSDs, which leaks user queries through disk access patterns to the host. Oblivious RAM (ORAM) can hide these access patterns but at a high cost; when paired with existing disk-based ANN search techniques, it makes poor use of SSD resources, yielding high latency and poor cost-efficiency. The core challenge for efficient oblivious ANN search over SSDs is balancing both bandwidth and access count. The state-of-the-art ORAM-ANN design minimizes access count at the ANN level and bandwidth at the ORAM level, each trading-off the other, leaving the combined system with both resources overutilized. We propose inverting this design, minimizing bandwidth consumption in the ANN layer and access count in the ORAM layer, since each component is better suited for its new role: ANN's inherent approximation allows for more bandwidth efficiency, while ORAM has no fundamental lower bounds on access count (as opposed to bandwidth). To this end, we propose a cost-efficient approach, Onyx, with two new co-designed components: Onyx-ANNS introduces a compact intermediate representation that proactively prunes the majority of bandwidth-intensive accesses without hurting recall, and Onyx-ORAM proposes a locality-aware shallow tree design that reduces access count while remaining compatible with bandwidth-efficient ORAM techniques. Compared to the state-of-the-art oblivious ANN search system, Onyx achieves $1.7-9.9\times$ lower cost and $2.3-12.3\times$ lower latency.

cs.CR↗

arXiv2025

Optimizing CPU Cache Utilization in Cloud VMs with Accurate Cache Abstraction

This paper shows that cache-based optimizations are often ineffective in cloud virtual machines (VMs) due to limited visibility into and control over provisioned caches. In public clouds, CPU caches can be partitioned or shared among VMs, but a VM is unaware of cache provisioning details. Moreover, a VM cannot influence cache usage via page placement policies, as memory-to-cache mappings are hidden. The paper proposes a novel solution, CacheX, which probes accurate and fine-grained cache abstraction within VMs using eviction sets without requiring hardware or hypervisor support, and showcases the utility of the probed information with two new techniques: LLC contention-aware task scheduling and virtual color-aware page cache management. Our evaluation of CacheX's implementation in x86 Linux kernel demonstrates that it can effectively improve cache utilization for various workloads in public cloud VMs.

cs.DC↗

arXiv2024

Last-Level Cache Side-Channel Attacks Are Feasible in the Modern Public Cloud (Extended Version)

Last-level cache side-channel attacks have been mostly demonstrated in highly-controlled, quiescent local environments. Hence, it is unclear whether such attacks are feasible in a production cloud environment. In the cloud, side channels are flooded with noise from activities of other tenants and, in Function-as-a-Service (FaaS) workloads, the attacker has a very limited time window to mount the attack. In this paper, we show that such attacks are feasible in practice, although they require new techniques. We present an end-to-end, cross-tenant attack on a vulnerable ECDSA implementation in the public FaaS Google Cloud Run environment. We introduce several new techniques to improve every step of the attack. First, to speed-up the generation of eviction sets, we introduce L2-driven candidate address filtering and a Binary Search-based algorithm for address pruning. Second, to monitor victim memory accesses with high time resolution, we introduce Parallel Probing. Finally, we leverage power spectral density from signal processing to easily identify the victim's target cache set in the frequency domain. Overall, using these mechanisms, we extract a median value of 81% of the secret ECDSA nonce bits from a victim container in 19 seconds on average.

cs.CR↗

arXiv2023

DECLASSIFLOW: A Static Analysis for Modeling Non-Speculative Knowledge to Relax Speculative Execution Security Measures (Full Version)

Speculative execution attacks undermine the security of constant-time programming, the standard technique used to prevent microarchitectural side channels in security-sensitive software such as cryptographic code. Constant-time code must therefore also deploy a defense against speculative execution attacks to prevent leakage of secret data stored in memory or the processor registers. Unfortunately, contemporary defenses, such as speculative load hardening (SLH), can only satisfy this strong security guarantee at a very high performance cost. This paper proposes DECLASSIFLOW, a static program analysis and protection framework to efficiently protect constant-time code from speculative leakage. DECLASSIFLOW models "attacker knowledge" -- data which is inherently transmitted (or, implicitly declassified) by the code's non-speculative execution -- and statically removes protection on such data from points in the program where it is already guaranteed to leak non-speculatively. Overall, DECLASSIFLOW ensures that data which never leaks during the non-speculative execution does not leak during speculative execution, but with lower overhead than conservative protections like SLH.

cs.CR↗