Opal is a private memory system for personal AI that maintains high retrieval accuracy and throughput while ensuring data privacy by confining all data-dependent reasoning to a trusted hardware enclav…

Cloak is an oblivious storage system that significantly improves the performance of ORAM by exploiting temporal locality, achieving low overheads while maintaining security.

TENNOR is a system that enables efficient and private training of wide neural networks in untrusted cloud environments by using doubly oblivious primitives and a novel memory-efficient hashing scheme.

ACRONYM is a novel algorithm-hardware co-designed platform that enables high-recall, continuous approximate nearest neighbor search in memory for dynamic vector databases, achieving massive throughput…

Tessera introduces a novel hardware architecture that achieves secure, near-line-rate weight streaming for DNNs on UMA edge accelerators by performing cache-line granularity decryption during DRAM fet…

The paper characterizes Homomorphic Encryption (HE) operations on a real-world Processing-In-Memory (PIM) system, demonstrating that while PIM is a viable alternative to CPUs/GPUs, performance is limi…

The paper proposes a Privacy-Preserving Product-Quantization Approximate Nearest Neighbor (PPPQ-ANN) framework that achieves practical performance and strong privacy guarantees for large-scale nearest…

This paper empirically evaluates the performance of the Polars DataFrame engine running within Intel SGX2 enclaves, finding that while the overall security overhead is manageable, the performance is s…

The paper introduces PoSME, a cryptographic primitive that enforces strict sequential memory execution by chaining data-dependent writes, providing verifiable delay and authorship attestation.

This paper investigates the potential of real-world Processing-in-Memory (PIM) architectures, specifically using UPMEM, to accelerate cryptographic algorithms, demonstrating that distributing computat…

The paper proposes PrISM, an intersection-based probabilistic mitigation technique that significantly improves the scalability of RowHammer defense at low thresholds by correlating sampled row history…

The paper systematically evaluates various defense mechanisms against persistent memory attacks on LLM agents, finding that only tool-gating at the memory layer (Memory Sandbox) effectively mitigates…

The paper presents a highly optimized, low-stack implementation of the HAETAE signature scheme, reducing peak stack usage significantly to enable its use on severely memory-constrained microcontroller…

GPIR is a GPU-accelerated Private Information Retrieval (PIR) system that significantly boosts throughput by introducing a stage-aware hybrid execution model and optimizing data layouts for modern GPU…

The paper introduces PVAC, a novel victim-based row counting mechanism that accurately tracks RowHammer attacks by incrementing counters on the victim row, thereby improving hammering tolerance and pe…

The paper characterizes 'dead-entry' TLB misses in GPUs, which occur when recently evicted translations are immediately re-walked, and proposes DEPOT, a Bloom filter mechanism that significantly reduc…

OpenEye is a scalable, sparsity-aware FPGA-based hardware accelerator designed to efficiently execute common deep neural network operations, demonstrating favorable performance-resource trade-offs acr…

MemLineage introduces a novel, cryptographically-backed defense mechanism that enforces a chain-of-custody for LLM agent memory, preventing untrusted or poisoned state from justifying sensitive action…

The paper introduces Appraisal, a novel Screening-then-Linkage framework (PPRS) that significantly improves the scalability and efficiency of Privacy-Preserving Record Linkage by incorporating a light…

MemPrivacy introduces a novel framework that protects sensitive user data in edge-cloud memory systems by replacing private spans with semantically structured placeholders, thereby minimizing data exp…