Privacy

Poisson Private Representation (PPR) enables exact compression of any local differential privacy mechanism, achieving order-wise optimal trade-offs between communication, accuracy, and privacy.

This paper presents a novel framework for achieving tighter differential privacy guarantees via mechanism-specific amplification using subsampling.

This paper presents a novel, truthful, and privacy-preserving mechanism for high-dimensional sparse linear regression, incentivizing data contribution while safeguarding individual privacy.

Trap-MID: Outsmarting model inversion attacks with cleverly placed ’trapdoors'!

This paper reveals fundamental limits of differential privacy in online learning, demonstrating a clear separation between pure, approximate, and non-private settings.

This paper reveals significant privacy gaps in shuffling-based DP-SGD, proposes a scalable Poisson subsampling method, and demonstrates its superior utility for private model training.

Researchers achieve a breakthrough in privacy-preserving machine learning by developing sample-efficient algorithms for learning Gaussian Mixture Models, significantly reducing the data needed while m…

Differentially private ReLU regression algorithms, DP-GLMtron and DP-TAGLMtron, achieve comparable performance with only an additional factor of O(log N) in the utility upper bound compared to the con…

InfoScissors defends collaborative inference from data leakage by cleverly reducing the mutual information between model outputs and sensitive device data, thus ensuring robust privacy without comprom…

Deleting data from machine learning models exposes individuals to highly accurate reconstruction attacks, even when models are simple; this research demonstrates the vulnerability.

Leveraging public data enhances differentially private (DP) learning, but its limits are unclear. This paper establishes tight theoretical bounds for DP stochastic convex optimization, revealing when …

Pseudo-Private Data Guided Model Inversion (PPDG-MI) significantly improves model inversion attacks by dynamically tuning the generative model to increase the sampling probability of actual private da…

PrivCirNet accelerates private deep learning inference by cleverly transforming DNN weights into circulant matrices, converting matrix-vector multiplications into efficient 1D convolutions suitable fo…

Achieving near-optimal rates for differentially private stochastic convex optimization with heavy-tailed gradients is possible using simple reduction-based techniques.

New transformation boosts privacy in online learning!

This paper introduces new differentially private algorithms to compute the geometric median, achieving improved accuracy by scaling with the effective data diameter instead of a known radius.

This paper delivers a groundbreaking polynomial-time algorithm for optimally estimating edge density in random graphs while ensuring node privacy and robustness against data corruption.

Train high-quality generative models with strong differential privacy using a novel slicing mechanism that injects noise into random low-dimensional data projections, avoiding noisy gradients.

Researchers reveal ‘privacy backdoors,’ a new attack that exploits pre-trained models to leak user training data, highlighting critical vulnerabilities and prompting stricter model security measures.

This paper introduces a Bayesian approach to setting the privacy budget in differential privacy, enabling agencies to balance data utility and confidentiality by customizing risk profiles.