The paper proposes a novel method to automatically enforce differential privacy in stream-based runtime monitoring specifications by analyzing temporal dependencies and injecting calibrated noise.

This paper introduces personalized mechanisms for estimating streaming statistics under $w$-event personalized differential privacy, significantly improving accuracy compared to existing methods.

The paper introduces a novel realization-level privacy filtering approach that improves utility in differentially private data release by accounting for actual leakage rather than worst-case per-round…

The paper demonstrates that by introducing carefully designed correlations among locally added noise variables, local differential privacy mechanisms can achieve an estimation cost matching the optima…

The paper proposes a dual-regime architecture combining Bernoulli CUSUM and asymmetric scoring to significantly improve trust fraud detection in sparse rating networks, achieving superior performance…

The paper introduces PolyVeil, a protocol for private Boolean summation that uses permutation matrices in the Birkhoff polytope, achieving strong security guarantees while highlighting a fundamental t…

The paper introduces novel, efficient differentially private algorithms for estimating monotone statistics, significantly improving sample complexity compared to existing methods.

This paper demonstrates that standard privacy guarantees for multi-tenant RAG services fail when multiple accounts from the same tenant collude, proposing a novel audit protocol to quantify this joint…

The paper proposes a robust causal decision framework to measure advertising incrementality despite multiple sources of privacy-induced signal degradation, providing certified decisions on the strengt…

CALIBURN introduces a novel, five-component streaming pipeline for intrusion detection that allows operators to specify alerting behavior using cost and budget constraints, achieving state-of-the-art…

The paper introduces Metric-Normalized Posterior Leakage (mPL), an attacker-aligned measure that provides a practical, certifiable privacy guarantee for machine learning systems consumed under joint o…

The paper characterizes the optimal achievable rate for differentially private hypothesis testing using e-values, providing an exact algorithm for both fixed and sequential settings.

LAPRAS proposes a learning-augmented differentially private query answering framework that uses predictions of future queries to maximize utility while maintaining robustness against prediction errors…

The paper proposes a distributed, privacy-preserving monitoring architecture that uses secret-sharing to efficiently monitor systems with continuous state, overcoming the scalability issues of traditi…

The paper introduces an optimal black-box auditing framework using Donsker-Varadhan estimators to estimate Rényi differential privacy (RDP) guarantees for machine learning algorithms.

The paper presents the first general mechanism for counting arbitrary acyclic graph patterns under Local Differential Privacy (LDP), addressing challenges in pattern construction and node duplication.

This paper corrects the theoretical analysis of DP-SGD by identifying that common implementations, which use batch averaging, result in weaker privacy guarantees than previously reported.

The paper introduces Fractional-Order Differentially Private Stochastic Gradient Descent (FO-DP-SGD), a mechanism that incorporates fractional memory into the gradient release process to improve priva…

This paper addresses the challenging problem of multi-objective submodular maximization under a cardinality constraint while ensuring differential privacy, proposing novel algorithms with approximatio…

This paper extends the privacy subsidy concept from the single-period Kyle model to continuous time, deriving a closed-form expression for the cumulative expected transfer (privacy subsidy) in a conti…