Related papers: Blindfold: Confidential Memory Management by Untru…
This paper introduces a vision of confidential prompting: securing user prompts from an untrusted, cloud-hosted large language model (LLM) while preserving model confidentiality, output invariance, and compute efficiency. As a first step…
Cache attacks pose a threat to any code whose execution flow or memory accesses depend on sensitive information. Especially in public clouds, where caches are shared across several tenants, cache attacks remain an unsolved problem. Cache…
Confidential computing protects data in use within Trusted Execution Environments (TEEs), but current TEEs provide little support for secure communication between components. As a result, pipelines of independently developed and deployed…
Blind quantum computing (BQC) is a computational paradigm that allows a client with limited quantum capabilities to delegate quantum computations to a more powerful server while keeping both the algorithm and data hidden. However, in…
In cloud computing, data processing is delegated to a remote party for efficiency and flexibility reasons. A practical user requirement usually is that the confidentiality and integrity of data processing needs to be protected. In the…
Memory corruption vulnerabilities often enable attackers to take control of a target system by overwriting control-flow relevant data (such as return addresses and function pointers), which are potentially stored in close proximity of…
Known protocols for secure delegation of quantum computations from a client to a server in an information theoretic setting require quantum communication. In this work, we investigate methods to reduce communication overhead. First, we…
This paper proposes GuardNN, a secure DNN accelerator that provides hardware-based protection for user data and model parameters even in an untrusted environment. GuardNN shows that the architecture and protection can be customized for a…
We present a new least-privilege-based model of addressing on which to base memory management functionality in an OS for modern computers like phones or server-based accelerators. Existing software assumptions do not account for…
Users regularly enter sensitive data, such as passwords, credit card numbers, or tax information, into the browser window. While modern browsers provide powerful client-side privacy measures to protect this data, none of these defenses…
The proliferation of large AI models trained on uncurated, often sensitive web-scraped data has raised significant privacy concerns. One of the concerns is that adversaries can extract information about the training data using privacy…
NVIDIA GPU Confidential Computing (GPU-CC) aims to provide secure execution for AI workloads. For end users, enabling GPU-CC is seamless and requires no modifications to existing applications. However, this ease of adoption relies on a…
Multi-tenant computing platforms are typically comprised of several software and hardware components including platform firmware, host operating system kernel, virtualization monitor, and the actual tenant payloads that run on them…
This paper describes a possible way to improve computer security by implementing a program which implements the following three features related to a weak notion of artificial consciousness: (partial) self-monitoring, ability to compute the…
This paper addresses privacy protection in decentralized Artificial Intelligence (AI) using Confidential Computing (CC) within the Atoma Network, a decentralized AI platform designed for the Web3 domain. Decentralized AI distributes AI…
Many existing Artificial Intelligence (AI) solutions on mobile devices rely on an extensive collection of sensitive data, raising privacy concerns and often requiring storage for both context and model improvement. Apple's Private Cloud…
Previous schemes for designing secure branch prediction unit (SBPU) based on physical isolation can only offer limited security and significantly affect BPU's prediction capability, leading to prominent performance degradation. Moreover,…
Confidential computing is a key technology for isolating high-assurance applications from the large amounts of untrusted code typical in modern systems. Existing confidential computing systems cannot be certified for use in critical…
Trusted Computing is a security base technology that will perhaps be ubiquitous in a few years in personal computers and mobile devices alike. Despite its neutrality with respect to applications, it has raised some privacy concerns. We show…
With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable…