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This paper develops a framework for privatizing the spectrum of the Laplacian of an undirected graph using differential privacy. We consider two privacy formulations. The first obfuscates the presence of edges in the graph and the second obfuscates the presence of nodes. We compare these two privacy formulations and show that the privacy formulation that considers edges is better suited to most engineering applications. We use the bounded Laplace mechanism to provide (epsilon, delta)-differential privacy to the eigenvalues of a graph Laplacian, and we pay special attention to the algebraic connectivity, which is the Laplacian's the second smallest eigenvalue. Analytical bounds are presented on the accuracy of the mechanisms and on certain graph properties computed with private spectra. A suite of numerical examples confirms the accuracy of private spectra in practice. 

Stochastic matrices are commonly used to analyze Markov chains, but revealing them can leak sensitive information. Therefore, in this paper we introduce a technique to privatize stochastic matrices in a way that (i) conceals the probabilities they contain, and (ii) still allows for accurate analyses of Markov chains. Specifically, we use differential privacy, which is a statistical framework for protecting sensitive data. To implement it, we introduce the Matrix Dirichlet Mechanism, which is a probabilistic mapping that perturbs a stochastic matrix to provide privacy. We prove that this mechanism provides differential privacy, and we quantify the error induced in private stochastic matrices as a function of the strength of privacy being provided. We then bound the distance between the stationary distribution of the underlying, sensitive stochastic matrix and the stationary distribution of its privatized form. Numerical results show that, under typical conditions, privacy introduces error as low as 5.05% in the stationary distribution of a stochastic matrix. 

In the past, researchers designed, deployed, and evaluated Wi-Fi based localization techniques in order to locate users and devices without adding extra or costly infrastructure. However, as infrastructure deployments change, one must reexamine the role of Wi-Fi localization. Today, cameras are becoming increasingly deployed, and therefore this work examines how contextual and vision data obtained from cameras can be integrated with Wi-Fi localization techniques. We present an approach called CALM that works on commodity APs and cameras. Our approach contains several contributions: a camera line fitting technique to restrict the search space of candidate locations, single AP and camera localization via a deprojection scheme inspired from 3D cameras, simple and robust AP weighting that analyzes the context of users via the camera, and a new virtual camera methodology to scale analysis. We motivate our scheme by analyzing real camera and AP topologies from a major vendor. Our evaluation over 9 rooms and 102,300 wireless readings shows CALM can obtain decimeter-level accuracy, improving performance over previous Wi-Fi techniques like FTM by 2.7× and SpotFi by 2.3×. 

Mobile computing devices have been used to store and process sensitive or even mission critical data. To protect sensitive data in mobile devices, encryption is usually incorporated into major mobile operating systems. However, traditional encryption can not defend against coercive attacks in which victims are forced to disclose the key used to decrypt the sensitive data. To combat the coercive attackers, plausibly deniable encryption (PDE) has been introduced which can allow the victims to deny the existence of the sensitive data. However, the existing PDE systems designed for mobile devices are either insecure (i.e., suffering from deniability compromises) or impractical (i.e., unable to be compatible with the storage architecture of mainstream mobile devices, not lightweight, or not user-oriented). In this work, we design CrossPDE, the first cross-layer mobile PDE system which is secure, being compatible with the storage architecture of mainstream mobile devices, lightweight as well as user-oriented. Our key idea is to intercept major layers of a mobile storage system, including the file system layer (preventing loss of hidden sensitive data and enabling users to use the hidden mode), the block layer (taking care of expensive encryption and decryption), and the flash translation layer (eliminating traces caused by the hidden sensitive data). Experimental evaluation on our real-world prototype shows that CrossPDE can ensure deniability with a modest decrease in throughput. 

Ransomware is increasingly prevalent in recent years. To defend against ransomware in computing devices using flash memory as external storage, existing designs extract the entire raw flash memory data to restore the external storage to a good state. However, they cannot allow a fine-grained recovery in terms of user files as raw flash memory data do not have the semantics of "files". In this work, we design FFRecovery, a new ransomware defense strategy that can support fine-grained data recovery after the attacks. Our key idea is, to recover a file corrupted by the ransomware, we can 1) restore its file system metadata via file system forensics, and 2) extract its file data via raw data extraction from the flash translation layer, and 3) assemble the corresponding file system metadata and the file data. A simple prototype of FFRecovery has been developed and some preliminary results are provided.