Search Results for author:
Found 15 papers, 7 papers with code
Closed-Form Bounds for DP-SGD against Record-level Inference
This paper presents a new approach to evaluate the privacy of machine learning models against specific record-level threats, such as membership and attribute inference, without the indirection through DP.
Maatphor: Automated Variant Analysis for Prompt Injection Attacks
This tool can also assist in generating datasets for jailbreak and prompt injection attacks, thus overcoming the scarcity of data in this domain.
Rethinking Privacy in Machine Learning Pipelines from an Information Flow Control Perspective
In this paper, we take an information flow control perspective to describe machine learning systems, which allows us to leverage metadata such as access control policies and define clear-cut privacy and confidentiality guarantees with interpretable information flows.
On the Efficacy of Differentially Private Few-shot Image Classification
There has been significant recent progress in training differentially private (DP) models which achieve accuracy that approaches the best non-private models.
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
Deploying machine learning models in production may allow adversaries to infer sensitive information about training data.
Bayesian Estimation of Differential Privacy
Our Bayesian method exploits the hypothesis testing interpretation of differential privacy to obtain a posterior for $\varepsilon$ (not just a confidence interval) from the joint posterior of the false positive and false negative rates of membership inference attacks.
Grey-box Extraction of Natural Language Models
This is true even for queries that are entirely in-distribution, making extraction attacks indistinguishable from legitimate use; (ii) with fine-tuned base layers, the effectiveness of algebraic attacks decreases with the learning rate, showing that fine-tuning is not only beneficial for accuracy but also indispensable for model confidentiality.
Analyzing Information Leakage of Updates to Natural Language Models
To continuously improve quality and reflect changes in data, machine learning applications have to regularly retrain and update their core models.
PDoT: Private DNS-over-TLS with TEE Support
Security and privacy of the Internet Domain Name System (DNS) have been longstanding concerns.
Cryptography and Security
S-FaaS: Trustworthy and Accountable Function-as-a-Service using Intel SGX
A core contribution of S-FaaS is our set of resource measurement mechanisms that securely measure compute time inside an enclave, and actual memory allocations.
Cryptography and Security
Mitigating Branch-Shadowing Attacks on Intel SGX using Control Flow Randomization
Intel Software Guard Extensions (SGX) is a promising hardware-based technology for protecting sensitive computations from potentially compromised system software.
Cryptography and Security
Keys in the Clouds: Auditable Multi-device Access to Cryptographic Credentials
Personal cryptographic keys are the foundation of many secure services, but storing these keys securely is a challenge, especially if they are used from multiple devices.
Cryptography and Security
Towards Linux Kernel Memory Safety
The security of billions of devices worldwide depends on the security and robustness of the mainline Linux kernel.
Cryptography and Security Operating Systems
Formal Analysis of V2X Revocation Protocols
This paper focusses on the formal analysis of a particular element of security mechanisms for V2X found in many proposals: the revocation of malicious or misbehaving vehicles from the V2X system by invalidating their credentials.
Cryptography and Security D.2.4; D.4.6
C-FLAT: Control-FLow ATtestation for Embedded Systems Software
Remote attestation is a crucial security service particularly relevant to increasingly popular IoT (and other embedded) devices.
Cryptography and Security
