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.

Collaborative and distributed applications, such as dynamic coalitions and virtualized grid computing, often require integrating access control policies of collaborating parties. Such an integration must be able to support complex authorization specifications and the fine-grained integration requirements that the various parties may have. In this paper, we introduce an algebra for fine-grained integration of sophisticated policies. The algebra, which consists of three binary and two unary operations, is able to support the specification of a large variety of integration constraints. To assess the expressive power of our algebra, we introduce a notion of completeness and prove that our algebra is complete with respect to this notion. We then propose a framework that uses the algebra for the fine-grained integration of policies expressed in XACML. We also present a methodology for generating the actual integrated XACML policy, based on the notion of Multi-Terminal Binary Decision Diagrams.

/pdf/an-algebra-for-fine-grained-integration-of-xacml-policies-1o2b4rvblz.pdf

An algebra for fine-grained integration of XACML policies

A framework supporting the privacy policy life cycle helps guide the kind of research to consider before sound privacy answers may be realized.

https://www.cs.purdue.edu/homes/ninghui/papers/roadmap.pdf

A roadmap for comprehensive online privacy policy management

Existing work on privacy-preserving data publishing cannot satisfactorily prevent an adversary with background knowledge from learning important sensitive information. The main challenge lies in modeling the adversary's background knowledge. We propose a novel approach to deal with such attacks. In this approach, one first mines knowledge from the data to be released and then uses the mining results as the background knowledge when anonymizing the data. The rationale of our approach is that if certain facts or background knowledge exist, they should manifest themselves in the data and we should be able to find them using data mining techniques. One intriguing aspect of our approach is that one can argue that it improves both privacy and utility at the same time, as it both protects against background knowledge attacks and better preserves the features in the data. We then present the Injector framework for data anonymization. Injector mines negative association rules from the data to be released and uses them in the anonymization process. We also develop an efficient anonymization algorithm to compute the injected tables that incorporates background knowledge. Experimental results show that Injector reduces privacy risks against background knowledge attacks while improving data utility.

/pdf/injector-mining-background-knowledge-for-data-anonymization-2q4cgs8uaz.pdf

Injector: Mining Background Knowledge for Data Anonymization

Databases are increasingly being used to store information covered by heterogeneous policies, which require support for access control with great flexibility. This has led to increasing interest in using fine-grained access control, where different cells in a relation may be governed by different access control rules. Although several proposals have been made to support fine-grained access control, there currently does not exist a formal notion of correctness regarding the query answering procedure. In this paper, we propose such a formal notion of correctness in fine-grained database access control, and discuss why existing approaches fall short in some circumstances. We then propose a labeling approach for masking unauthorized information and a query evaluation algorithm which better supports fine-grained access control. Finally, we implement our algorithm using query modification and evaluate its performance.

https://www.vldb.org/conf/2007/papers/research/p555-wang.pdf

On the correctness criteria of fine-grained access control in relational databases

Exchange of digitally signed certificates is often used to establish mutual trust between strangers that wish to share resources or to conduct business transactions. Automated Trust Negotiation (ATN) is an approach to regulate the flow of sensitive information during such an exchange. Previous work on ATN are based on access control techniques, and cannot handle cyclic policy interdependency satisfactorily. We show that the problem can be modelled as a 2-party secure function evaluation (SFE) problem, and propose a scheme called oblivious signature-based envelope (OSBE) for efficiently solving the SFE problem. We develop a provably secure and efficient OSBE protocol for certificates signed using RSA signatures. We also build provably secure and efficient one-round OSBE for Rabin and BLS signatures from recent constructions for identity-based encryption. We also discuss other applications of OSBE.

/pdf/oblivious-signature-based-envelope-5drcy1eg1m.pdf

Ninghui Li

Papers

An algebra for fine-grained integration of XACML policies

A roadmap for comprehensive online privacy policy management

Injector: Mining Background Knowledge for Data Anonymization

On the correctness criteria of fine-grained access control in relational databases

Oblivious signature-based envelope