Post-Processing in Local Differential Privacy: an Extensive Evaluation and Benchmark Platform

Local differential privacy LDP has recently gained prominence as a powerful paradigm for collecting and analyzing sensitive data from users' devices. However, the inherent perturbation added by LDP protocols reduces the utility of the collected data. To mitigate this issue, several post-processin...

The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation

Large Language Models LLMs have demonstrated remarkable capabilities across a wide range of tasks, yet they also exhibit memorization of their training data. This phenomenon raises critical questions about model behavior, privacy risks, and the boundary between learning and memorization. Addressi...

Efficient Unlearning with Privacy Guarantees

Privacy protection laws, such as the GDPR, grant individuals the right to request the forgetting of their personal data not only from databases but also from machine learning ML models trained on them. Machine unlearning has emerged as a practical means to facilitate model forgetting of data...

Model Inversion Attacks on Llama 3: Extracting PII from Large Language Models

Large language models LLMs have transformed natural language processing, but their ability to memorize training data poses significant privacy risks. This paper investigates model inversion attacks on the Llama 3.2 model, a multilingual LLM developed by Meta. By querying the model with carefully...

UniAud: a Unified Auditing Framework for High Auditing Power and Utility with One Training Run

Differentially private DP optimization has been widely adopted as a standard approach to provide rigorous privacy guarantees for training datasets. DP auditing verifies whether a model trained with DP optimization satisfies its claimed privacy level by estimating empirical privacy lower bounds...

RVISmith: Fuzzing Compilers for RVV Intrinsics

Modern processors are equipped with single instruction multiple data SIMD instructions for fine-grained data parallelism. Compiler auto-vectorization techniques that target SIMD instructions face performance limitations due to insufficient information available at compile time, requiring...

Balancing Privacy and Utility in Correlated Data: a Study of Bayesian Differential Privacy

Privacy risks in differentially private DP systems increase significantly when data is correlated, as standard DP metrics often underestimate the resulting privacy leakage, leaving sensitive information vulnerable. Given the ubiquity of dependencies in real-world databases, this oversight poses a...

PrivacyGo: Privacy-Preserving Ad Measurement with Multidimensional Intersection

This paper tackles the challenging and practical problem of multi-identifier private user profile matching for privacy-preserving ad measurement, a cornerstone of modern advertising analytics. We introduce a comprehensive cryptographic framework leveraging reversed Oblivious Pseudorandom Function...

Empowering Digital Agriculture: a Privacy-Preserving Framework for Data Sharing and Collaborative Research

Data-driven agriculture, which integrates technology and data into agricultural practices, has the potential to improve crop yield, disease resilience, and long-term soil health. However, privacy concerns, such as adverse pricing, discrimination, and resource manipulation, deter farmers from...

Don'T Hash Me like That: Exposing and Mitigating Hash-Induced Unfairness in Local Differential Privacy

Local differential privacy LDP has become a widely accepted framework for privacy-preserving data collection. In LDP, many protocols rely on hash functions to implement user-side encoding and perturbation. However, the security and privacy implications of hash function selection have not been...

Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-To-Peer Learning

The growing adoption of Artificial Intelligence AI in Internet of Things IoT ecosystems has intensified the need for personalized learning methods that can operate efficiently and privately across heterogeneous, resource-constrained devices. However, enabling effective personalized learning in...

Communication-Efficient Publication of Sparse Vectors under Differential Privacy

Whitepaper called Communication-Efficient Publication Of Sparse Vectors Under Differential Privacy...

Machine Learning with Privacy for Protected Attributes

Differential privacy DP has become the standard for private data analysis. Certain machine learning applications only require privacy protection for specific protected attributes. Using naive variants of differential privacy in such use cases can result in unnecessary degradation of utility. In...

Can One Safety Loop Guard Them All? Agentic Guard Rails for Federated Computing

We propose Guardian-FC, a novel two-layer framework for privacy preserving federated computing that unifies safety enforcement across diverse privacy preserving mechanisms, including cryptographic back-ends like fully homomorphic encryption FHE and multiparty computation MPC, as well as statistic...

Network Structures As an Attack Surface: Topology-Based Privacy Leakage in Federated Learning

Federated learning systems increasingly rely on diverse network topologies to address scalability and organizational constraints. While existing privacy research focuses on gradient-based attacks, the privacy implications of network topology knowledge remain critically understudied. We conduct th...

Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models

Are there any conditions under which a generative model's outputs are guaranteed not to infringe the copyrights of its training data? This is the question of "provable copyright protection" first posed by Vyas, Kakade, and Barak ICML 2023. They define near access-freeness NAF and propose it as...

Differential Privacy in Machine Learning: from Symbolic AI to LLMs

Machine learning models should not reveal particular information that is not otherwise accessible. Differential privacy provides a formal framework to mitigate privacy risks by ensuring that the inclusion or exclusion of any single data point does not significantly alter the output of an algorith...

Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It?

Inherent communication noises have the potential to preserve privacy for wireless federated learning WFL but have been overlooked in digital communication systems predominantly using floating-point number standards, e.g., IEEE 754, for data storage and transmission. This is due to the potentially...

LLM-Based Dynamic Differential Testing for Database Connectors with Reinforcement Learning-Guided Prompt Selection

Database connectors are critical components enabling applications to interact with underlying database management systems DBMS, yet their security vulnerabilities often remain overlooked. Unlike traditional software defects, connector vulnerabilities exhibit subtle behavioral patterns and are...

Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning

Differential privacy DP is obtained by randomizing a data analysis algorithm, which necessarily introduces a tradeoff between its utility and privacy. Many DP mechanisms are built upon one of two underlying tools: Laplace and Gaussian additive noise mechanisms. We expand the search space of...