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.

Regularly releasing the aggregate statistics about data streams in a privacy-preserving way not only serves valuable commercial and social purposes, but also protects the privacy of individuals. This problem has already been studied under differential privacy, but only for the case of a single continuous query that covers the entire time span, e.g., counting the number of tuples seen so far in the stream. However, most real-world applications are window-based, that is, they are interested in the statistical information about streaming data within a window, instead of the whole unbound stream. Furthermore, a Data Stream Management System (DSMS) may need to answer numerous correlated aggregated queries simultaneously, rather than a single one. To cope with these requirements, we study how to release differentially private answers for a set of sliding window aggregate queries. We propose two solutions, each consisting of query sampling and composition. We first selectively sample a subset of representative sliding window queries from the set of all the submitted ones. The representative queries are answered by adding Laplace noises in a way satisfying differential privacy. For each non-representative query, we compose its answer from the query results of those representatives. The experimental evaluation shows that our solutions are efficient and effective.

/pdf/efficient-and-accurate-strategies-for-differentially-private-2zwsakwp38.pdf

Efficient and accurate strategies for differentially-private sliding window queries

We study the membership inference (MI) attack against classifiers, where the attacker's goal is to determine whether a data instance was used for training the classifier. Through systematic cataloging of existing MI attacks and extensive experimental evaluations of them, we find that a model's vulnerability to MI attacks is tightly related to the generalization gap -- the difference between training accuracy and test accuracy. We then propose a defense against MI attacks that aims to close the gap by intentionally reduces the training accuracy. More specifically, the training process attempts to match the training and validation accuracies, by means of a new {\em set regularizer} using the Maximum Mean Discrepancy between the softmax output empirical distributions of the training and validation sets. Our experimental results show that combining this approach with another simple defense (mix-up training) significantly improves state-of-the-art defense against MI attacks, with minimal impact on testing accuracy.

Membership Inference Attacks and Defenses in Classification Models

Delegation is a mechanism that allows a user Ato act on another user B's behalf by making B's access rights available to A. It is well recognized as an important mechanism to provide resiliency and flexibility in access control systems, and has gained popularity in the research community. However, most existing literature focuses on modeling and managing delegations. Little work has been done on understanding the impact of delegation on the security of existing access control systems. In particular, no formal notion of security with respect to delegation has been proposed. Many existing access control systems are designed without having delegation in mind. Simply incorporating a delegation module into those systems may cause security breaches.

This paper focuses on the security aspect of delegation in access control systems. We first give examples on how colluding users may abuse the delegation support of access control systems to circumvent security policies, such as separation of duty. As a major contribution, we propose a formal notion of security with respect to delegation in access control systems. After that, we discuss potential mechanisms to enforce security. In particular, we design a novel source-based enforcement mechanism for workflow authorization systems so as to achieve both security and efficiency.

/pdf/on-the-security-of-delegation-in-access-control-systems-3hwm6cqflf.pdf

On the Security of Delegation in Access Control Systems

In this paper we analyze the requirements access control mechanisms must fulfill in the context of group communication and define a framework for supporting fine-grained access control in client-server group communication systems. Our framework combines rolebased access control mechanisms with environment parameters (time, IP address, etc.) to provide support for a wide range of applications with very different requirements. While the access control policy is defined by the application, its efficient enforcement is provided by the group communication system.

/pdf/a-framework-for-role-based-access-control-in-group-4m6zo4g1t3.pdf

A Framework for Role-Based Access Control in Group Communication Systems.

Differential privacy protects an individual’s privacy by perturbing data on an aggregated level (DP) or individual level (LDP). We report four online human-subject experiments investigating the effects of using different approaches to communicate differential privacy techniques to laypersons in a health app data collection setting. Experiments 1 and 2 investigated participants’ data disclosure decisions for low-sensitive and high-sensitive personal information when given different DP or LDP descriptions. Experiments 3 and 4 uncovered reasons behind participants’ data sharing decisions, and examined participants’ subjective and objective comprehensions of these DP or LDP descriptions. When shown descriptions that explain the implications instead of the definition/processes of DP or LDP technique, participants demonstrated better comprehension and showed more willingness to share information with LDP than with DP, indicating their understanding of LDP’s stronger privacy guarantee compared with DP.

/pdf/towards-effective-differential-privacy-communication-for-4g21iubj57.pdf

Ninghui Li

Papers

Efficient and accurate strategies for differentially-private sliding window queries

Membership Inference Attacks and Defenses in Classification Models

On the Security of Delegation in Access Control Systems

A Framework for Role-Based Access Control in Group Communication Systems.

Towards Effective Differential Privacy Communication for Users’ Data Sharing Decision and Comprehension