Faking Sensor Noise Information

Noise residue detection in digital images has recently been used as a method to classify images based on source camera model type. The meteoric rise in the popularity of using Neural Network models has also been used in conjunction with the concept of noise residuals to classify source camera models. However, many papers gloss over the details on the methods of obtaining noise residuals and instead rely on the self- learning aspect of deep neural networks to implicitly discover this themselves. For this project I propose a method of obtaining noise residuals (“noiseprints”) and denoising an image, as well as a Generative model that can learn how to reproduce noise resembling a target digital camera model’s noise noiseprint. Applying a noiseprint generated by this model onto a denoised image will be able to fool a discriminating model into classifying the wrong digital camera model. To the best of my knowledge, this is the first work that will explicitly detail denoising methods and noiseprint generation in a 128 by 128 resolution for specific camera models and individual cameras for the goal of fooling a classification model

Justice in the Air: Tracking Toxic Pollution from America's Industries and Companies to Our States, Cities, and Neighborhoods

This new environmental justice study, (co-authored by PERI’s James Boyce, Michael Ash, & Grace Chang, along with Manuel Pastor, Justin Scoggins, & Jennifer Tran of the Program for Environmental and Regional Equity at the University of Southern California) examines not only who receives the disproportionate share of toxic air releases -- low-income communities and people of color -- but who is releasing them.� � Justice in the Air: Tracking Toxic Pollution from America's Industries and Companies to Our States, Cities, and Neighborhoods uses the EPA's Toxic Release Inventory and Risk Screening Environmental Indicators to explore the demographics of those who are most affected by toxic pollution, and then establishes the corporate ownership of the plants responsible.� � Justice in the Air enhances the data available in PERI’s Toxic 100 Report with a new environmental justice scorecard, ranking the Toxic 100 companies by the share of their health impacts from toxic air pollution that falls upon minority and low-income communities. The authors conclude by recommending four ways the right-to-know and environmental justice movements can use these findings in their efforts to protect the health of vulnerable communities. �

Spin dynamics across the superfluid-insulator transition of spinful bosons

Bosons with non-zero spin exhibit a rich variety of superfluid and insulating phases. Most phases support coherent spin oscillations, which have been the focus of numerous recent experiments. These spin oscillations are Rabi oscillations between discrete levels deep in the insulator, while deep in the superfluid they can be oscillations in the orientation of a spinful condensate. We describe the evolution of spin oscillations across the superfluid-insulator quantum phase transition. For transitions with an order parameter carrying spin, the damping of such oscillations is determined by the scaling dimension of the composite spin operator. For transitions with a spinless order parameter and gapped spin excitations, we demonstrate that the damping is determined by an associated quantum impurity problem of a localized spin excitation interacting with the bulk critical modes. We present a renormalization group analysis of the quantum impurity problem, and discuss the relationship of our results to experiments on ultracold atoms in optical lattices.Comment: 43 pages (single-column format), 8 figures; v2: corrected discussion of fixed points in Section V

Privacy-Preserving Sequential Pattern Mining Over Vertically Partitioned Data

Privacy-preserving data mining in distributed environments is an important issue in the field of data mining. In this paper, we study how to conduct sequential patterns mining, which is one of the data mining computations, on private data in the following scenario: Multiple parties, each having a private data set, want to jointly conduct sequential pattern mining. Since no party wants to disclose its private data to other parties, a secure method needs to be provided to make such a computation feasible. We develop a practical solution to the above problem in this paper

Privacy-Preserving Decision Tree Classification over Horizontally Partitioned Data

Protection of privacy is one of important problems in data mining. The unwillingness to share their data frequently results in failure of collaborative data mining. This paper studies how to build a decision tree classifier under the following scenario: a database is horizontally partitioned into multiple pieces, with each piece owned by a particular party. All the parties want to build a decision tree classifier based on such a database, but due to the privacy constraints, neither of them wants to disclose their private pieces. We build a privacy-preserving system, including a set of secure protocols, that allows the parties to construct such a classifier. We guarantee that the private data are securely protected

Privacy-Preserving Naive Bayesian Classification Over Vertically Partitioned Data

Protection of privacy is a critical problem in data mining. Preserving data privacy in distributed data mining is even more challenging. In this paper, we consider the problem of privacy-preserving naive Bayesian classification over vertically partitioned data. The problem is one of important issues in privacypreserving distributed data mining. Our approach is based on homomorphic encryption. The scheme is very efficient in the term of computation and communication cost

Privacy-Preserving Support Vector Machines Learning

This paper addresses the problem of data sharing among multiple parties, without disclosing the data between the parties. We focus on sharing of data among parties involved in a data mining task. We study how to share private or confidential data in the following scenario: without disclosing their private data to each other, multiple parties, each having a private data set, want to collaboratively construct support vector machines using a linear, polynomial or sigmoid kernel function. To tackle this problem, we develop a secure protocol for multiple parties to conduct the desired computation. The solution is distributed, i.e., there is no central, trusted party having access to all the data. Instead, we define a protocol using homomorphic encryption techniques to exchange the data while keeping it private. We analyze the protocol in the context of mistakes and malicious attacks, and show its robustness against such attacks. All the parties are treated symmetrically: they all participate in the encryption and in the computation involved in learning support vector machines

Bayesian Network Induction with Incomplete Private Data

A Bayesian network is a graphical model for representing probabilistic relationships among a set of variables. It is an important model for business analysis. Bayesian network learning methods have been applied to business analysis where data privacy is not considered. However, how to learn a Bayesian network over private data presents a much greater challenge. In this paper, we develop an approach to tackle the problem of Bayesian network induction on private data which may contain missing values. The basic idea of our proposed approach is that we combine randomization technique with Expectation Maximization (EM) algorithm. The purpose of using randomization is to disguise the raw data. EM algorithm is applied for missing values in the private data set. We also present a method to conduct Bayesian network construction, which is one of data mining computations, from the disguised data