There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

In several distributed systems a user should only be able to access data if a user posses a certain set of credentials or attributes. Currently, the only method for enforcing such policies is to employ a trusted server to store the data and mediate access control. However, if any server storing the data is compromised, then the confidentiality of the data will be compromised. In this paper we present a system for realizing complex access control on encrypted data that we call ciphertext-policy attribute-based encryption. By using our techniques encrypted data can be kept confidential even if the storage server is untrusted; moreover, our methods are secure against collusion attacks. Previous attribute-based encryption systems used attributes to describe the encrypted data and built policies into user's keys; while in our system attributes are used to describe a user's credentials, and a party encrypting data determines a policy for who can decrypt. Thus, our methods are conceptually closer to traditional access control methods such as role-based access control (RBAC). In addition, we provide an implementation of our system and give performance measurements.

/pdf/ciphertext-policy-attribute-based-encryption-q09jxdyx4n.pdf

Ciphertext-Policy Attribute-Based Encryption

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Smart mobile devices have become indispensable, yet the associated risks are not fully understood or known by users (Felt, Greenwood, & Wagner, 2011), and an effective way of communicating the risk information to the users is needed. In addition to a summary risk/safety index that has been shown to be effective (Chen, Gates, Li, & Proctor, 2015; Gates, Chen, Li, & Proctor, 2014), prior research (Jorgensen, Chen, Gates, Li, Proctor, & Yu, 2015) proposed an intermediate-level risk display that includes selected major risk categories that are identified by individual users (personal privacy; monetary loss; device stability). The goal of the present study was to evaluate the various formats for displaying these major risk categories, by comparing bar graph and numerical table representations (see Tversky, 1997), as well as the horizontal and vertical display orientations in each format. These displays were tested in two user tasks: Risk rating and app selection. In Experiment 1, participants were asked to rate the potential risk of an app based on the provided risk display. The bar graphs took shorter time and elicited higher risk ratings than the numerical tables. As the risk score increased in each category, the potential risk of the app was rated higher overall, and the self-reported mostly concerned risk category was weighed more than others. In Experiment 2, participants were required to choose one app that they would install out of two alternative apps given the potential risks of the two apps. There was no significant difference in terms of response time or app-selection percentage between the display formats. The apps with lower risk scores were chosen more often, and this effect again was influenced by the self-reported risk concerns. No difference was found between horizontal and vertical displays in either experiment. These results indicate that it is potentially beneficial to include this intermediate-level risk display in the app interface, and individualized design incorporating self-reported risk concerns of the users may be more informative. Moreover, the bar graph display seems to be more efficient and effective than the table display, at least when users need to evaluate an individual app.

http://pro.sagepub.com/content/60/1/779.full.pdf

Display of Major Risk Categories for Mobile Apps

Image classifiers often suffer from adversarial examples, which are generated by strategically adding a small amount of noise to input images to trick classifiers into misclassification. Over the years, many defense mechanisms have been proposed, and different researchers have made seemingly contradictory claims on their effectiveness. We present an analysis of possible adversarial models, and propose an evaluation framework for comparing different defense mechanisms. As part of the framework, we introduce a more powerful and realistic adversary strategy. Furthermore, we propose a new defense mechanism called Random Spiking (RS), which generalizes dropout and introduces random noises in the training process in a controlled manner. Evaluations under our proposed framework suggest RS delivers better protection against adversarial examples than many existing schemes.

Random Spiking and Systematic Evaluation of Defenses Against Adversarial Examples

Communicating Differential Privacy Models by Illustrations: A Survey and In-Depth Interview Study

Android filesystem access control provides a foundation for Android system integrity. Android utilizes a combination of mandatory (e.g., SEAndroid) and discretionary (e.g., UNIX permissions) access control, both to protect the Android platform from Android/OEM services and to protect Android/OEM services from third-party apps. However, OEMs often create vulnerabilities when they introduce market-differentiating features because they err when re-configuring this complex combination of Android policies. In this paper, we propose the PolyScope tool to triage the combination of Android filesystem access control policies to vet releases for vulnerabilities. The PolyScope approach leverages two main insights: (1) adversaries may exploit the coarse granularity of mandatory policies and the flexibility of discretionary policies to increase the permissions available to launch attacks, which we call permission expansion, and (2) system configurations may limit the ways adversaries may use their permissions to launch attacks, motivating computation of attack operations. We apply PolyScope to three Google and five OEM Android releases to compute the attack operations accurately to vet these releases for vulnerabilities, finding that permission expansion increases the permissions available to launch attacks, sometimes by more than 10X, but a significant fraction of these permissions (about 15-20%) are not convertible into attack operations. Using PolyScope, we find two previously unknown vulnerabilities, showing how PolyScope helps OEMs triage the complex combination of access control policies down to attack operations worthy of testing.

PolyScope: Multi-Policy Access Control Analysis to Triage Android Systems.

We summarize the experience of participating in two differential privacy competitions organized by the National Institute of Standards and Technology (NIST). In this paper, we document our experiences in the competition, the approaches we have used, the lessons we have learned, and our call to the research community to further bridge the gap between theory and practice in DP research.

Ninghui Li

Papers

Display of Major Risk Categories for Mobile Apps

Random Spiking and Systematic Evaluation of Defenses Against Adversarial Examples

Communicating Differential Privacy Models by Illustrations: A Survey and In-Depth Interview Study

PolyScope: Multi-Policy Access Control Analysis to Triage Android Systems.

DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challenges