arXiv subjects

Adil Ahmad

Publications and source records attributed to Adil Ahmad.

5 recordsLinked to original sources

arXiv2026

Reducio: Optimized Confidential Serverless Cloud Deployments for Enterprise Customers

Serverless platforms based on Confidential Virtual Machines (CVMs) have been recently proposed to address the privacy problems with serverless functions, while achieving low latency. Unfortunately, our study indicates that to achieve these properties, existing proposals impose non-trivial requirements in terms of infrastructure changes and platform memory. Reducio is an alternate serverless platform design that does not require infrastructure changes and significantly reduces platform memory requirements. The platform is designed using two key components: (1) a function isolation framework inside a CVM based on kernel deprivileging features that minimize infrastructure requirements, and (2) a layer-wise caching methodology and algorithm that effectively uses a small in-memory function cache. Our evaluation indicates that Reducio can significantly reduce both platform requirements for deployment and function memory consumption.

cs.CR↗

arXiv2026

Shoot the Honey, Cloak the Player: Towards Zero-Runtime-Overhead Proactive Defense and Detection for Visual Game Cheating

Visual aimbots have emerged as a serious cheating threat in first-person shooter (FPS) games, as they evade existing anti-cheat defenses by operating only on rendered frames rather than game memory. However, existing defenses fail to provide an end-to-end solution: post-hoc behavior detectors cannot protect match integrity in real time and are increasingly fragile against human-mimicking aimbots, while proactive runtime defenses often lack accountability, incur substantial overhead, or require intrusive system integration. We present AimTrap, the first end-to-end visual-aimbot defense that combines runtime protection with post-game detection through two adversarial texture mechanisms. Adversarial Camouflage Textures (ACT) hide real players from aimbots, while Adversarial Honeypot Textures (AHT) lure aimbots into locking onto fake targets, yielding strong evidence of cheating. AimTrap integrates differentiable rendering with Expectation over Renderings for robust 3D texture synthesis and analyzes honeypot-interaction trajectory to facilitate cheating attribution. In real-game evaluation against a visual aimbot, ACT achieves 85.1% defense success, AHT achieves 96.9%. Compared with prior baselines, AimTrap also achieves extremely low false-positive rates with negligible runtime overhead, demonstrating a practical end-to-end defense.

cs.CR↗

arXiv2024

The HitchHiker's Guide to High-Assurance System Observability Protection with Efficient Permission Switches

Protecting system observability records (logs) from compromised OSs has gained significant traction in recent times, with several note-worthy approaches proposed. Unfortunately, none of the proposed approaches achieve high performance with tiny log protection delays. They also leverage risky environments for protection (\eg many use general-purpose hypervisors or TrustZone, which have large TCB and attack surfaces). HitchHiker is an attempt to rectify this problem. The system is designed to ensure (a) in-memory protection of batched logs within a short and configurable real-time deadline by efficient hardware permission switching, and (b) an end-to-end high-assurance environment built upon hardware protection primitives with debloating strategies for secure log protection, persistence, and management. Security evaluations and validations show that HitchHiker reduces log protection delay by 93.3--99.3% compared to the state-of-the-art, while reducing TCB by 9.4--26.9X. Performance evaluations show HitchHiker incurs a geometric mean of less than 6% overhead on diverse real-world programs, improving on the state-of-the-art approach by 61.9--77.5%.

cs.CR↗

arXiv2019

A Tale of Two Trees: One Writes, and Other Reads. Optimized Oblivious Accesses to Large-Scale Blockchains

The Bitcoin network has offered a new way of securely performing financial transactions over the insecure network. Nevertheless, this ability comes with the cost of storing a large (distributed) ledger, which has become unsuitable for personal devices of any kind. Although the simplified payment verification (SPV) clients can address this storage issue, a Bitcoin SPV client has to rely on other Bitcoin nodes to obtain its transaction history and the current approaches offer no privacy guarantees to the SPV clients. This work presents $T^3$, a trusted hardware-secured Bitcoin full client that supports efficient oblivious search/update for Bitcoin SPV clients without sacrificing the privacy of the clients. In this design, we leverage the trusted execution and attestation capabilities of a trusted execution environment (TEE) and the ability to hide access patterns of oblivious random access memory (ORAM) to protect SPV clients' requests from a potentially malicious server. The key novelty of $T^3$ lies in the optimizations introduced to conventional ORAM, tailored for expected SPV client usages. In particular, by making a natural assumption about the access patterns of SPV clients, we are able to propose a two-tree ORAM construction that overcomes the concurrency limitation associated with traditional ORAMs. We have implemented and tested our system using the current Bitcoin Unspent Transaction Output database. Our experiment shows that the system is feasible to be deployed in practice while providing strong privacy and security guarantees to Bitcoin SPV clients.

cs.CR↗

arXiv2019

The Unconstrained Ear Recognition Challenge

In this paper we present the results of the Unconstrained Ear Recognition Challenge (UERC), a group benchmarking effort centered around the problem of person recognition from ear images captured in uncontrolled conditions. The goal of the challenge was to assess the performance of existing ear recognition techniques on a challenging large-scale dataset and identify open problems that need to be addressed in the future. Five groups from three continents participated in the challenge and contributed six ear recognition techniques for the evaluation, while multiple baselines were made available for the challenge by the UERC organizers. A comprehensive analysis was conducted with all participating approaches addressing essential research questions pertaining to the sensitivity of the technology to head rotation, flipping, gallery size, large-scale recognition and others. The top performer of the UERC was found to ensure robust performance on a smaller part of the dataset (with 180 subjects) regardless of image characteristics, but still exhibited a significant performance drop when the entire dataset comprising 3,704 subjects was used for testing.

cs.CV↗