arXiv subjects

Sean Peisert

Publications and source records attributed to Sean Peisert.

At least 19 recordsLinked to original sources

arXiv2026

Reconstruction Limits for Repeated Differentially Private Aggregates: A Cramer-Rao Perspective on Query Geometry

Repeated differentially private (DP) releases are often evaluated by transcript length or cumulative privacy accounting. We show that these quantities do not by themselves determine local reconstruction risk. For Gaussian-calibrated repeated statistical queries, the key object is the nuisance-profiled Fisher geometry of the release sequence: repetition helps only when new releases create identifiable directions after nuisance variables are removed. Thus, release geometry determines what can be locally identified, while the privacy accountant determines how precisely those directions can be estimated. We develop this principle in two settings. For labeled-target reconstruction with fixed-background IN/OUT averages, repeated copies collapse to a single target-versus-background contrast. The best linear unbiased estimator attains the Cramer-Rao bound, and additional copies provide only averaging gain; under Basic Composition this gain is dominated by the $\Theta(L^2\log L)$ noise penalty, whereas zCDP/RDP-style Gaussian accounting makes the risk order-flat. For static permutation-invariant releases, labels remain unidentified, but feature diversity can make the sorted participating multiset locally identifiable. For polynomial moments and smooth thresholds, the useful number of releases is governed by the balance between newly exposed eigendirections and accountant-induced noise growth. These results provide a local, mechanism-specific benchmark for value leakage in repeated private sensing and analytics.

cs.IT↗

arXiv2026

Differentially Private Synthetic Voltage Phasor Release for Distribution Grids

Training machine learning models, including Grid Foundation Models (GFMs), requires large volumes of realistic grid data, yet substantial privacy concerns discourage utilities and data providers from sharing load profiles and network parameters. We study the release of synthetic voltage phasor trajectories for distribution grids under differential privacy (DP). We first fit a DP generative model to historical customer loads, then propagate synthetic load trajectories through the AC power flow equations on the true admittance matrix to produce voltage phasors. The central question is whether the randomness already present in the DP synthetic loads is sufficient to protect not only the loads, but also the network topology encoded by the bus admittance matrix. We show that it is. The implication is that a corpus of voltage trajectories can be constructed from DP synthetic loads while preserving the statistics of AC power flow, which is critical for training GFMs. This preservation of the power flow statistics stands in contrast to approaches that perturb the admittance matrix directly or inject noise into the voltage outputs, both of which distort the underlying physics. Concretely, we derive $(\varepsilon,\delta)$-DP guarantees for the released voltage trajectories with respect to the admittance matrix, meaning privacy of the network parameters is obtained without any additional noise mechanism. Our bound depends on the adjacency assumption, the Jacobian of the AC power flow, and the covariance of the synthetic DP-loads. Finally, we present a synthetic voltage generation procedure and an empirical evaluation against Gaussian output-perturbation baselines, demonstrating that our approach provides a clear advantage for enabling GFM training.

eess.SY↗

arXiv2026

Space-Control: Process-Level Isolation for Sharing CXL-based Disaggregated Memory

Memory disaggregation via CXL enables multi-host resource sharing. However, existing CXL sharing mechanisms enforce coarse-grained, host-level permissions only, leaving isolation to the operating system. Today, virtual memory enables process-level isolation on a host and CXL enables host-level isolation. This creates a critical security gap: the absence of process-level memory isolation in shared disaggregated memory. We present Space-Control, an architectural abstraction that introduces a cross-host identity primitive to enforce confidentiality and integrity. We decouple authorization from the untrusted OS using a hardware-rooted validation engine (SPACE) to establish immutable process identity and a Permission Checker at the memory egress point for fine-grained permission validation. Our design supports 127 concurrent processes across 255 hosts with only 1.56% storage overhead. Cycle-level evaluation using gem5 + SST shows that Space-Control incurs a minimal 3.3% performance penalty with a modest 16 KiB cache, providing a practical and scalable foundation for secure, process-level memory disaggregation.

cs.AR↗

arXiv2026

Inter-detector differential fuzz testing for tamper detection in gamma spectrometers

We extend physical differential fuzz testing as an anti-tamper method for radiation detectors [Vavrek et al., Science and Global Security 2025] to comparisons across multiple detector units. The method was previously introduced as a tamper detection method for authenticating a single radiation detector in nuclear safeguards and treaty verification scenarios, and works by randomly sampling detector configuration parameters to produce a sequence of spectra that form a baseline signature of an untampered system. At a later date, after potential tampering, the same random sequence of parameters is used to generate another series of spectra that can be compared against the baseline. Anomalies in the series of comparisons indicate changes in detector behavior, which may be due to tampering. One limitation of this original method is that once the detector has `gone downrange' and may have been tampered with, the original baseline is fixed, and a new trusted baseline can never be established if tests at new parameters are required. In this work, we extend our anti-tamper fuzz testing concept to multiple detector units, such that the downrange detector can be compared against a trusted or `golden copy' detector, even despite normal inter-detector manufacturing variations. We show using three NaI detectors that this inter-detector differential fuzz testing can detect a representative attack, even when the tested and golden copy detectors are from different manufacturers and have different performances. Here, detecting tampering requires visualizing the comparison metric vs. the parameter values and not just the sample number; moreover this baseline is non-linear and may require anomaly detection methods more complex than a simple threshold. Overall, this extension to multiple detectors improves prospects for operationalizing the technique in real-world treaty verification and safeguards contexts.

physics.ins-det↗

arXiv2025

Differential Privacy of Network Parameters from a System Identification Perspective

This paper addresses the problem of protecting network information from privacy system identification (SI) attacks when sharing cyber-physical system simulations. We model analyst observations of networked states as time-series outputs of a graph filter driven by differentially private (DP) nodal excitations, with the analyst aiming to infer the underlying graph shift operator (GSO). Unlike traditional SI, which estimates system parameters, we study the inverse problem: what assumptions prevent adversaries from identifying the GSO while preserving utility for legitimate analysis. We show that applying DP mechanisms to inputs provides formal privacy guarantees for the GSO, linking the $(\epsilon,\delta)$-DP bound to the spectral properties of the graph filter and noise covariance. More precisely, for DP Gaussian signals, the spectral characteristics of both the filter and noise covariance determine the privacy bound, with smooth filters and low-condition-number covariance yielding greater privacy.

cs.CR↗

arXiv2025

Decentralized Differentially Private Power Method

We propose a novel Decentralized Differentially Private Power Method (D-DP-PM) for performing Principal Component Analysis (PCA) in networked multi-agent settings. Unlike conventional decentralized PCA approaches where each agent accesses the full n-dimensional sample space, we address the challenging scenario where each agent observes only a subset of dimensions through row-wise data partitioning. Our method ensures $(\epsilon,\delta)$-Differential Privacy (DP) while enabling collaborative estimation of global eigenvectors across the network without requiring a central aggregator. We achieve this by having agents share only local embeddings of the current eigenvector iterate, leveraging both the inherent privacy from random initialization and carefully calibrated Gaussian noise additions. We prove that our algorithm satisfies the prescribed $(\epsilon,\delta)$-DP guarantee and establish convergence rates that explicitly characterize the impact of the network topology. Our theoretical analysis, based on linear dynamics and high-dimensional probability theory, provides tight bounds on both privacy and utility. Experiments on real-world datasets demonstrate that D-DP-PM achieves superior privacy-utility tradeoffs compared to naive local DP approaches, with particularly strong performance in moderate privacy regimes ($\epsilon\in[2, 5]$). The method converges rapidly, allowing practitioners to trade iterations for enhanced privacy while maintaining competitive utility.

cs.LG↗

arXiv2025

Differentially Private Distribution Release of Gaussian Mixture Models via KL-Divergence Minimization

Gaussian Mixture Models (GMMs) are widely used statistical models for representing multi-modal data distributions, with numerous applications in data mining, pattern recognition, data simulation, and machine learning. However, recent research has shown that releasing GMM parameters poses significant privacy risks, potentially exposing sensitive information about the underlying data. In this paper, we address the challenge of releasing GMM parameters while ensuring differential privacy (DP) guarantees. Specifically, we focus on the privacy protection of mixture weights, component means, and covariance matrices. We propose to use Kullback-Leibler (KL) divergence as a utility metric to assess the accuracy of the released GMM, as it captures the joint impact of noise perturbation on all the model parameters. To achieve privacy, we introduce a DP mechanism that adds carefully calibrated random perturbations to the GMM parameters. Through theoretical analysis, we quantify the effects of privacy budget allocation and perturbation statistics on the DP guarantee, and derive a tractable expression for evaluating KL divergence. We formulate and solve an optimization problem to minimize the KL divergence between the released and original models, subject to a given $(\epsilon, \delta)$-DP constraint. Extensive experiments on both synthetic and real-world datasets demonstrate that our approach achieves strong privacy guarantees while maintaining high utility.

cs.IT↗

arXiv2024

Differential fuzz testing to detect tampering in sensor systems and its application to arms control authentication

In future nuclear arms control treaties, it will be necessary to authenticate the hardware and software components of verification measurement systems, i.e., to ensure these systems are functioning as intended and have not been tampered with by malicious actors. While methods such as source code hashing and static analysis can help verify the integrity of software components, they may not be capable of detecting tampering with environment variables, external libraries, or the firmware and hardware of radiation measurement systems. In this article, we introduce the concept of physical differential fuzz testing as a challenge-response-style tamper indicator that can holistically and simultaneously test all the above components in a cyber-physical system. In essence, we randomly sample (or "fuzz") the untampered system's parameter space, including both normal and off-normal parameter values, and consider the time series of outputs as the baseline signature of the system. Re-running the same input sequence on a untampered system will produce an output sequence consistent with this baseline, while running the same input sequence on a tampered system will produce a modified output sequence and raise an alarm. We then apply this concept to authenticating the radiation measurement equipment in nuclear weapon verification systems and conduct demonstration fuzz testing measurements with a sodium iodide (NaI) gamma ray spectrometer. Because there is Poisson noise in the measured output spectra, we also use a mechanism for comparing inherently noisy or stochastic fuzzing sequences. We show that physical differential fuzz testing can detect two types of tamper attempts, and conclude that it is a promising framework for authenticating future cyber-physical systems in nuclear arms control, safeguards, and beyond.

physics.ins-det↗

arXiv2024

Differentially Private Communication of Measurement Anomalies in the Smart Grid

In this paper, we present a framework based on differential privacy (DP) for querying electric power measurements to detect system anomalies or bad data. Our DP approach conceals consumption and system matrix data, while simultaneously enabling an untrusted third party to test hypotheses of anomalies, such as the presence of bad data, by releasing a randomized sufficient statistic for hypothesis-testing. We consider a measurement model corrupted by Gaussian noise and a sparse noise vector representing the attack, and we observe that the optimal test statistic is a chi-square random variable. To detect possible attacks, we propose a novel DP chi-square noise mechanism that ensures the test does not reveal private information about power injections or the system matrix. The proposed framework provides a robust solution for detecting bad data while preserving the privacy of sensitive power system data.

eess.SP↗

arXiv2024

Differential Privacy in Aggregated Mobility Networks: Balancing Privacy and Utility

Location data is collected from users continuously to understand their mobility patterns. Releasing the user trajectories may compromise user privacy. Therefore, the general practice is to release aggregated location datasets. However, private information may still be inferred from an aggregated version of location trajectories. Differential privacy (DP) protects the query output against inference attacks regardless of background knowledge. This paper presents a differential privacy-based privacy model that protects the user's origins and destinations from being inferred from aggregated mobility datasets. This is achieved by injecting Planar Laplace noise to the user origin and destination GPS points. The noisy GPS points are then transformed into a link representation using a link-matching algorithm. Finally, the link trajectories form an aggregated mobility network. The injected noise level is selected using the Sparse Vector Mechanism. This DP selection mechanism considers the link density of the location and the functional category of the localized links. Compared to the different baseline models, including a k-anonymity method, our differential privacy-based aggregation model offers query responses that are close to the raw data in terms of aggregate statistics at both the network and trajectory-levels with maximum 9% deviation from the baseline in terms of network length.

cs.CR↗

arXiv2023

Differential Privacy for Class-based Data: A Practical Gaussian Mechanism

In this paper, we present a notion of differential privacy (DP) for data that comes from different classes. Here, the class-membership is private information that needs to be protected. The proposed method is an output perturbation mechanism that adds noise to the release of query response such that the analyst is unable to infer the underlying class-label. The proposed DP method is capable of not only protecting the privacy of class-based data but also meets quality metrics of accuracy and is computationally efficient and practical. We illustrate the efficacy of the proposed method empirically while outperforming the baseline additive Gaussian noise mechanism. We also examine a real-world application and apply the proposed DP method to the autoregression and moving average (ARMA) forecasting method, protecting the privacy of the underlying data source. Case studies on the real-world advanced metering infrastructure (AMI) measurements of household power consumption validate the excellent performance of the proposed DP method while also satisfying the accuracy of forecasted power consumption measurements.

eess.SP↗

arXiv2023

Solar Photovoltaic Systems Metadata Inference and Differentially Private Publication

Stakeholders in electricity delivery infrastructure are amassing data about their system demand, use, and operations. Still, they are reluctant to share them, as even sharing aggregated or anonymized electric grid data risks the disclosure of sensitive information. This paper highlights how applying differential privacy to distributed energy resource production data can preserve the usefulness of that data for operations, planning, and research purposes without violating privacy constraints. Differentially private mechanisms can be optimized for queries of interest in the energy sector, with provable privacy and accuracy trade-offs, and can help design differentially private databases for further analysis and research. In this paper, we consider the problem of inference and publication of solar photovoltaic systems' metadata. Metadata such as nameplate capacity, surface azimuth and surface tilt may reveal personally identifiable information regarding the installation behind-the-meter. We describe a methodology to infer the metadata and propose a mechanism based on Bayesian optimization to publish the inferred metadata in a differentially private manner. The proposed mechanism is numerically validated using real-world solar power generation data.

eess.SY↗

arXiv2022

Perspectives for self-driving labs in synthetic biology

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.

q-bio.OT↗

arXiv2022

Differentially Private $K$-means Clustering Applied to Meter Data Analysis and Synthesis

The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called `15/15 rule', to respond to the need. However, the current policies fail to adequately guarantee privacy. In this paper, we address the problem of allowing third parties to apply $K$-means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm's utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset.

eess.SP↗

arXiv2022

Adaptive Control of Distributed Energy Resources for Distribution Grid Voltage Stability

Volt-VAR and Volt-Watt functionality in photovoltaic (PV) smart inverters provide mechanisms to ensure system voltage magnitudes and power factors remain within acceptable limits. However, these control functions can become unstable, introducing oscillations in system voltages when not appropriately configured or maliciously altered during a cyberattack. In the event that Volt-VAR and Volt-Watt control functions in a portion of PV smart inverters in a distribution grid are unstable, the proposed adaptation scheme utilizes the remaining and stably-behaving PV smart inverters and other Distributed Energy Resources to mitigate the effect of the instability. The adaptation mechanism is entirely decentralized, model-free, communication-free, and requires virtually no external configuration. We provide a derivation of the adaptive control approach and validate the algorithm in experiments on the IEEE 37 and 8500 node test feeders.

eess.SY↗

arXiv2022

Adam-based Augmented Random Search for Control Policies for Distributed Energy Resource Cyber Attack Mitigation

Volt-VAR and Volt-Watt control functions are mechanisms that are included in distributed energy resource (DER) power electronic inverters to mitigate excessively high or low voltages in distribution systems. In the event that a subset of DER have had their Volt-VAR and Volt-Watt settings compromised as part of a cyber-attack, we propose a mechanism to control the remaining set of non-compromised DER to ameliorate large oscillations in system voltages and large voltage imbalances in real time. To do so, we construct control policies for individual non-compromised DER, directly searching the policy space using an Adam-based augmented random search (ARS). In this paper we show that, compared to previous efforts aimed at training policies for DER cybersecurity using deep reinforcement learning (DRL), the proposed approach is able to learn optimal (and sometimes linear) policies an order of magnitude faster than conventional DRL techniques (e.g., Proximal Policy Optimization).

eess.SY↗

arXiv2021

SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation

Fast and safe voltage regulation algorithms can serve as fundamental schemes for achieving a high level of renewable penetration in the modern distribution power grids. Faced with uncertain or even unknown distribution grid models and fast-changing power injections, model-free deep reinforcement learning (DRL) algorithms have been proposed to find the reactive power injections for inverters while optimizing the voltage profiles. However, such data-driven controllers can not guarantee satisfaction of the hard operational constraints, such as maintaining voltage profiles within a certain range of the nominal value. To this end, we propose SAVER: SAfe VoltagE Regulator, which is composed of an RL learner and a specifically designed, computational efficient safety projection layer. SAVER provides a plug-and-play interface for a set of DRL algorithms that guarantees the system voltages to be within safe bounds. Numerical simulations on real-world data validate the performance of the proposed algorithm.

eess.SY↗

arXiv2021

Learning from learning machines: a new generation of AI technology to meet the needs of science

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself.

cs.LG↗