arXiv subjects

Shantanu Rane

Publications and source records attributed to Shantanu Rane.

17 recordsLinked to original sources

arXiv2024

Delay-Induced Watermarking for Detection of Replay Attacks in Linear Systems

A state-feedback watermarking signal design for the detection of replay attacks in linear systems is proposed. The control input is augmented with a random time-delayed term of the system state estimate, in order to secure the system against attacks of replay type. We outline the basic analysis of the closed-loop response of the state-feedback watermarking in a LQG controlled system. Our theoretical results are applied on a temperature process control example. While the proposed secure control scheme requires very involved analysis, it, nevertheless, holds promise of being superior to conventional, feed-forward, watermarking schemes, in both its ability to detect attacks as well as the secured system performance.

eess.SY↗

arXiv2024

Differential Privacy in Nonlinear Dynamical Systems with Tracking Performance Guarantees

We introduce a novel approach to make the tracking error of a class of nonlinear systems differentially private in addition to guaranteeing the tracking error performance. We use funnel control to make the tracking error evolve within a performance funnel that is pre-specified by the user. We make the performance funnel differentially private by adding a bounded continuous noise generated from an Ornstein-Uhlenbeck-type process. Since the funnel controller is a function of the performance funnel, the noise adds randomized perturbation to the control input. We show that, as a consequence of the differential privacy of the performance funnel, the tracking error is also differentially private. As a result, the tracking error is bounded by the noisy funnel boundary while maintaining privacy. We show a simulation result to demonstrate the framework.

eess.SY↗

arXiv2024

System Design Approach for Control of Differentially Private Dynamical Systems

This paper introduces a novel approach to concurrently design dynamic controllers and correlated differential privacy noise in dynamic control systems. An increase in privacy noise increases the system's privacy but adversely affects the system's performance. Our approach optimizes the noise distribution while shaping closed-loop system dynamics such that the privacy noise has the least impact on system performance and the most effect on system privacy. We further add privacy noise to both control input and system output to privatize the system's state for an adversary with access to both communication channels and direct output measurements. The study also suggests tailored privacy bounds for different states, providing a comprehensive framework for jointly optimizing system performance and privacy in the context of differential privacy.

eess.SY↗

arXiv2023

Incentivizing Gigaton-Scale Carbon Dioxide Removal via a Climate-Positive Blockchain

A new crypto token is proposed as an incentive mechanism to remove CO2 from the atmosphere permanently at gigaton scale. The token facilitates CO2 removal (CDR) by providing financial incentives to those that are removing CO2 and an opportunity to provide additional financial resources for CDR by the public. The new token will be native to a blockchain that uses a Proof-of-Useful-Work (PoUW) consensus mechanism. The useful work will be conducted by direct air carbon capture and storage (DACCS) facilities that will compete with each other based on the amount of CO2 captured and permanently stored. In terms of energy consumption, we require that the entire process, comprising DACCS technology and all blockchain operations, be climate positive while accounting for life cycle analysis of equipment used. We describe the underlying reward mechanism coupled with a verification mechanism for CDR. In addition, we consider security features to limit attacks and fraudulent activity. Finally, we outline a roadmap of features that are necessary to fully implement and deploy such a system, but are beyond the current scope of this article.

cs.CR↗

arXiv2023

CONSTRUCT: A Program Synthesis Approach for Reconstructing Control Algorithms from Embedded System Binaries in Cyber-Physical Systems

We introduce a novel approach to automatically synthesize a mathematical representation of the control algorithms implemented in industrial cyber-physical systems (CPS), given the embedded system binary. The output model can be used by subject matter experts to assess the system's compliance with the expected behavior and for a variety of forensic applications. Our approach first performs static analysis on decompiled binary files of the controller to create a sketch of the mathematical representation. Then, we perform an evolutionary-based search to find the correct semantic for the created representation, i.e., the control law. We demonstrate the effectiveness of the introduced approach in practice via three case studies conducted on two real-life industrial CPS.

cs.SE↗

arXiv2022

Co-Design of Watermarking and Robust Control for Security in Cyber-Physical Systems

This work discusses a novel framework for simultaneous synthesis of optimal watermarking signal and robust controllers in cyber-physical systems to minimize the loss in performance due to added watermarking signal and to maximize the detection rate of the attack. A general dynamic controller is designed to improve system performance with respect to the $\mathcal H_2$ norm, while a watermarking signal is added to improve security performance concerning the detection rate of replay attacks. The attack model considered in the paper is a replay attack, a natural attack mode when the dynamics of the system is unknown to the attacker. The paper first generalizes the existing result on the detection rate of $\chi^2$ detector from a static-LQR controller to a general dynamic controller. The design improvements on both robustness and security fronts are obtained by iteratively solving the convex subsets of the formulated non-convex problem in terms of the controller and watermarking signal. A semi-definite programming optimization is formulated using Linear Matrix Inequality (LMI) results to solve the larger system-level design optimization problem. We highlight the effectiveness of our method over a simplified three-tank chemical system.

eess.SY↗

arXiv2022

Zero-Query Transfer Attacks on Context-Aware Object Detectors

Adversarial attacks perturb images such that a deep neural network produces incorrect classification results. A promising approach to defend against adversarial attacks on natural multi-object scenes is to impose a context-consistency check, wherein, if the detected objects are not consistent with an appropriately defined context, then an attack is suspected. Stronger attacks are needed to fool such context-aware detectors. We present the first approach for generating context-consistent adversarial attacks that can evade the context-consistency check of black-box object detectors operating on complex, natural scenes. Unlike many black-box attacks that perform repeated attempts and open themselves to detection, we assume a "zero-query" setting, where the attacker has no knowledge of the classification decisions of the victim system. First, we derive multiple attack plans that assign incorrect labels to victim objects in a context-consistent manner. Then we design and use a novel data structure that we call the perturbation success probability matrix, which enables us to filter the attack plans and choose the one most likely to succeed. This final attack plan is implemented using a perturbation-bounded adversarial attack algorithm. We compare our zero-query attack against a few-query scheme that repeatedly checks if the victim system is fooled. We also compare against state-of-the-art context-agnostic attacks. Against a context-aware defense, the fooling rate of our zero-query approach is significantly higher than context-agnostic approaches and higher than that achievable with up to three rounds of the few-query scheme.

cs.CV↗

arXiv2019

Secure Two-Party Feature Selection

In this work, we study how to securely evaluate the value of trading data without requiring a trusted third party. We focus on the important machine learning task of classification. This leads us to propose a provably secure four-round protocol that computes the value of the data to be traded without revealing the data to the potential acquirer. The theoretical results demonstrate a number of important properties of the proposed protocol. In particular, we prove the security of the proposed protocol in the honest-but-curious adversary model.

cs.CR↗

arXiv2016

Matrix Factorization-Based Clustering Of Image Features For Bandwidth-Constrained Information Retrieval

We consider the problem of accurately and efficiently querying a remote server to retrieve information about images captured by a mobile device. In addition to reduced transmission overhead and computational complexity, the retrieval protocol should be robust to variations in the image acquisition process, such as translation, rotation, scaling, and sensor-related differences. We propose to extract scale-invariant image features and then perform clustering to reduce the number of features needed for image matching. Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF) are investigated as candidate clustering approaches. The image matching complexity at the database server is quadratic in the (small) number of clusters, not in the (very large) number of image features. We employ an image-dependent information content metric to approximate the model order, i.e., the number of clusters, needed for accurate matching, which is preferable to setting the model order using trial and error. We show how to combine the hypotheses provided by PCA and NMF factor loadings, thereby obtaining more accurate retrieval than using either approach alone. In experiments on a database of urban images, we obtain a top-1 retrieval accuracy of 89% and a top-3 accuracy of 92.5%.

cs.CV↗

arXiv2015

Representation and Coding of Signal Geometry

Approaches to signal representation and coding theory have traditionally focused on how to best represent signals using parsimonious representations that incur the lowest possible distortion. Classical examples include linear and non-linear approximations, sparse representations, and rate-distortion theory. Very often, however, the goal of processing is to extract specific information from the signal, and the distortion should be measured on the extracted information. The corresponding representation should, therefore, represent that information as parsimoniously as possible, without necessarily accurately representing the signal itself. In this paper, we examine the problem of encoding signals such that sufficient information is preserved about their pairwise distances and their inner products. For that goal, we consider randomized embeddings as an encoding mechanism and provide a framework to analyze their performance. We also demonstrate that it is possible to design the embedding such that it represents different ranges of distances with different precision. These embeddings also allow the computation of kernel inner products with control on their inner product-preserving properties. Our results provide a broad framework to design and analyze embeddins, and generalize existing results in this area, such as random Fourier kernels and universal embeddings.

cs.IT↗

arXiv2014

An Elementary Completeness Proof for Secure Two-Party Computation Primitives

In the secure two-party computation problem, two parties wish to compute a (possibly randomized) function of their inputs via an interactive protocol, while ensuring that neither party learns more than what can be inferred from only their own input and output. For semi-honest parties and information-theoretic security guarantees, it is well-known that, if only noiseless communication is available, only a limited set of functions can be securely computed; however, if interaction is also allowed over general communication primitives (multi-input/output channels), there are "complete" primitives that enable any function to be securely computed. The general set of complete primitives was characterized recently by Maji, Prabhakaran, and Rosulek leveraging an earlier specialized characterization by Kilian. Our contribution in this paper is a simple, self-contained, alternative derivation using elementary information-theoretic tools.

cs.CR↗

arXiv2014

Unconditionally Secure Computation on Large Distributed Databases with Vanishing Cost

Consider a network of k parties, each holding a long sequence of n entries (a database), with minimum vertex-cut greater than t. We show that any empirical statistic across the network of databases can be computed by each party with perfect privacy, against any set of t < k/2 passively colluding parties, such that the worst-case distortion and communication cost (in bits per database entry) both go to zero as n, the number of entries in the databases, goes to infinity. This is based on combining a striking dimensionality reduction result for random sampling with unconditionally secure multi-party computation protocols.

cs.CR↗

arXiv2013

On Unconditionally Secure Multiparty Computation for Realizing Correlated Equilibria in Games

In game theory, a trusted mediator acting on behalf of the players can enable the attainment of correlated equilibria, which may provide better payoffs than those available from the Nash equilibria alone. We explore the approach of replacing the trusted mediator with an unconditionally secure sampling protocol that jointly generates the players' actions. We characterize the joint distributions that can be securely sampled by malicious players via protocols using error-free communication. This class of distributions depends on whether players may speak simultaneously ("cheap talk") or must speak in turn ("polite talk"). In applying sampling protocols toward attaining correlated equilibria with rational players, we observe that security against malicious parties may be much stronger than necessary. We propose the concept of secure sampling by rational players, and show that many more distributions are feasible given certain utility functions. However, the payoffs attainable via secure sampling by malicious players are a dominant subset of the rationally attainable payoffs.

cs.CR↗

arXiv2013

Secure Biometrics: Concepts, Authentication Architectures and Challenges

BIOMETRICS are an important and widely used class of methods for identity verification and access control. Biometrics are attractive because they are inherent properties of an individual. They need not be remembered like passwords, and are not easily lost or forged like identifying documents. At the same time, bio- metrics are fundamentally noisy and irreplaceable. There are always slight variations among the measurements of a given biometric, and, unlike passwords or identification numbers, biometrics are derived from physical characteristics that cannot easily be changed. The proliferation of biometric usage raises critical privacy and security concerns that, due to the noisy nature of biometrics, cannot be addressed using standard cryptographic methods. In this article we present an overview of "secure biometrics", also referred to as "biometric template protection", an emerging class of methods that address these concerns.

cs.CR↗

arXiv2013

On the Benefits of Sampling in Privacy Preserving Statistical Analysis on Distributed Databases

We consider a problem where mutually untrusting curators possess portions of a vertically partitioned database containing information about a set of individuals. The goal is to enable an authorized party to obtain aggregate (statistical) information from the database while protecting the privacy of the individuals, which we formalize using Differential Privacy. This process can be facilitated by an untrusted server that provides storage and processing services but should not learn anything about the database. This work describes a data release mechanism that employs Post Randomization (PRAM), encryption and random sampling to maintain privacy, while allowing the authorized party to conduct an accurate statistical analysis of the data. Encryption ensures that the storage server obtains no information about the database, while PRAM and sampling ensures individual privacy is maintained against the authorized party. We characterize how much the composition of random sampling with PRAM increases the differential privacy of system compared to using PRAM alone. We also analyze the statistical utility of our system, by bounding the estimation error - the expected l2-norm error between the true empirical distribution and the estimated distribution - as a function of the number of samples, PRAM noise, and other system parameters. Our analysis shows a tradeoff between increasing PRAM noise versus decreasing the number of samples to maintain a desired level of privacy, and we determine the optimal number of samples that balances this tradeoff and maximizes the utility. In experimental simulations with the UCI "Adult Data Set" and with synthetically generated data, we confirm that the theoretically predicted optimal number of samples indeed achieves close to the minimal empirical error, and that our analytical error bounds match well with the empirical results.

cs.CR↗

arXiv2013

Information-Theoretically Secure Three-Party Computation with One Corrupted Party

The problem in which one of three pairwise interacting parties is required to securely compute a function of the inputs held by the other two, when one party may arbitrarily deviate from the computation protocol (active behavioral model), is studied. An information-theoretic characterization of unconditionally secure computation protocols under the active behavioral model is provided. A protocol for Hamming distance computation is provided and shown to be unconditionally secure under both active and passive behavioral models using the information-theoretic characterization. The difference between the notions of security under the active and passive behavioral models is illustrated through the BGW protocol for computing quadratic and Hamming distances; this protocol is secure under the passive model, but is shown to be not secure under the active model.

cs.CR↗

arXiv2011

A Theoretical Analysis of Authentication, Privacy and Reusability Across Secure Biometric Systems

We present a theoretical framework for the analysis of privacy and security tradeoffs in secure biometric authentication systems. We use this framework to conduct a comparative information-theoretic analysis of two biometric systems that are based on linear error correction codes, namely fuzzy commitment and secure sketches. We derive upper bounds for the probability of false rejection ($P_{FR}$) and false acceptance ($P_{FA}$) for these systems. We use mutual information to quantify the information leaked about a user's biometric identity, in the scenario where one or multiple biometric enrollments of the user are fully or partially compromised. We also quantify the probability of successful attack ($P_{SA}$) based on the compromised information. Our analysis reveals that fuzzy commitment and secure sketch systems have identical $P_{FR}, P_{FA}, P_{SA}$ and information leakage, but secure sketch systems have lower storage requirements. We analyze both single-factor (keyless) and two-factor (key-based) variants of secure biometrics, and consider the most general scenarios in which a single user may provide noisy biometric enrollments at several access control devices, some of which may be subsequently compromised by an attacker. Our analysis highlights the revocability and reusability properties of key-based systems and exposes a subtle design tradeoff between reducing information leakage from compromised systems and preventing successful attacks on systems whose data have not been compromised.

cs.IT↗