Michael Carl Tschantz

Verification and change-impact analysis of access-control policies

Sensitive data are increasingly available on-line through the Web and other distributed protocols. This heightens the need to carefully control access to data. Control means not only preventing the leakage of data but also permitting access to necessary information. Indeed, the same datum is often treated differently depending on context. System designers create policies to express conditions on the access to data. To reduce source clutter and improve maintenance, developers increasingly use domain-specific, declarative languages to express these policies. In turn, administrators need to analyze policies relative to properties, and to understand the effect of policy changes even in the absence of properties. This paper presents Margrave, a software suite for analyzing role-based access-control policies. Margrave includes a verifier that analyzes policies written in the XACML language, translating them into a form of decision-diagram to answer queries. It also provides semantic differencing information between versions of policies. We have implemented these techniques and applied them to policies from a working software application.

Automated Experiments on Ad Privacy Settings

Abstract To partly address people’s concerns over web tracking, Google has created the Ad Settings webpage to provide information about and some choice over the profiles Google creates on users. We present AdFisher, an automated tool that explores how user behaviors, Google’s ads, and Ad Settings interact. AdFisher can run browser-based experiments and analyze data using machine learning and significance tests. Our tool uses a rigorous experimental design and statistical analysis to ensure the statistical soundness of our results. We use AdFisher to find that the Ad Settings was opaque about some features of a user’s profile, that it does provide some choice on ads, and that these choices can lead to seemingly discriminatory ads. In particular, we found that visiting webpages associated with substance abuse changed the ads shown but not the settings page. We also found that setting the gender to female resulted in getting fewer instances of an ad related to high paying jobs than setting it to male. We cannot determine who caused these findings due to our limited visibility into the ad ecosystem, which includes Google, advertisers, websites, and users. Nevertheless, these results can form the starting point for deeper investigations by either the companies themselves or by regulatory bodies.

Towards reasonability properties for access-control policy languages

The growing importance of access control has led to the definition of numerous languages for specifying policies. Since these languages are based on different foundations, language users and designers would benefit from formal means to compare them. We present a set of properties that examine the behavior of policies under enlarged requests, policy growth, and policy decomposition. They therefore suggest whether policies written in these languages are easier or harder to reason about under various circumstances. We then evaluate multiple policy languages, including XACML and Lithium, using these properties.

Botticelli: a supply chain management agent designed to optimize under uncertainty

The paper describes the design of the agent BOTTICELLI, a finalist in the 2003 Trading Agent Competition in Supply Chain Management (TAC SCM). In TAC SCM, a simulated computer manufacturing scenario, BOTTICELLI competes with other agents to win customer orders and negotiates with suppliers to procure the components necessary to complete its orders. We formalize subproblems that dictate BOTTICELLIs behavior. Stochastic programming approaches to bidding and scheduling are developed in attempt to solve these problems optimally. In addition, we describe greedy methods that yield useful approximations. Test results compare the performance and computational effciency of these two techniques.

Botticelli: A Supply Chain Management Agent

The paper describes the architecture of Brown Universityýs agent, Botticelli, a finalist in the 2003 Trading Agent Competition in Supply Chain Management (TAC SCM). In TAC SCM, a simulated computer manufacturing scenario, Botticelli competes with other agents to win customer orders and negotiates with suppliers to procure the components necessary to complete its orders. In this paper, two subproblems that dictate Botticelliýs behavior are formalized: bidding and scheduling. Mathematical programming approaches are applied in attempt to solve these problems optimally. In addition, greedy methods that yield useful approximations are described. Test results compare the performance and computational efficiency of these alternative techniques.

Approaches to adversarial drift

In this position paper, we argue that to be of practical interest, a machine-learning based security system must engage with the human operators beyond feature engineering and instance labeling to address the challenge of drift in adversarial environments. We propose that designers of such systems broaden the classification goal into an explanatory goal, which would deepen the interaction with systems operators. To provide guidance, we advocate for an approach based on maintaining one classifier for each class of unwanted activity to be filtered. We also emphasize the necessity for the system to be responsive to the operators constant curation of the training set. We show how this paradigm provides a property we call isolation and how it relates to classical causative attacks. In order to demonstrate the effects of drift on a binary classification task, we also report on two experiments using a previously unpublished malware data set where each instance is timestamped according to when it was seen.

Formal Methods for Privacy

Privacy means something different to everyone. Against a vast and rich canvas of diverse types of privacy rights and violations, we argue technologys dual role in privacy: new technologies raise new threats to privacy rights and new technologies can help preserve privacy. Formal methods, as just one class of technology, can be applied to privacy, but privacy raises new challenges, and thus new research opportunities, for the formal methods community.

Adversarial Active Learning

Active learning is an area of machine learning examining strategies for allocation of finite resources, particularly human labeling efforts and to an extent feature extraction, in situations where available data exceeds available resources. In this open problem paper, we motivate the necessity of active learning in the security domain, identify problems caused by the application of present active learning techniques in adversarial settings, and propose a framework for experimentation and implementation of active learning systems in adversarial contexts. More than other contexts, adversarial contexts particularly need active learning as ongoing attempts to evade and confuse classifiers necessitate constant generation of labels for new content to keep pace with adversarial activity. Just as traditional machine learning algorithms are vulnerable to adversarial manipulation, we discuss assumptions specific to active learning that introduce additional vulnerabilities, as well as present vulnerabilities that are amplified in the active learning setting. Lastly, we present a software architecture, Security-oriented Active Learning Testbed (SALT), for the research and implementation of active learning applications in adversarial contexts.

SoK: Towards Grounding Censorship Circumvention in Empiricism

Effective evaluations of approaches to circumventing government Internet censorship require incorporating perspectives of how censors operate in practice. We undertake an extensive examination of real censors by surveying prior measurement studies and analyzing field reports and bug tickets from practitioners. We assess both deployed circumvention approaches and research proposals to consider the criteria employed in their evaluations and compare these to the observed behaviors of real censors, identifying areas where evaluations could more faithfully and effectively incorporate the practices of modern censors. These observations lead to an agenda realigning research with the predominant problems of today.

Scaling up the sample average approximation method for stochastic optimization with applications to trading agents

The Sample Average Approximation (SAA) method is a technique for approximating solutions to stochastic programs. Here, we attempt to scale up the SAA method to harder problems than those previously studied. We argue that to apply the SAA method effectively, there are three parameters to optimize: the number of evaluations, the number of scenarios, and the number of candidate solutions. We propose an experimental methodology for finding the optimal settings of these parameters given fixed time and space constraints. We apply our methodology to two large-scale stochastic optimization problems that arise in the context of the annual Trading Agent Competition. Both problems are expressed as integer linear programs and solved using CPLEX. Runtime increases linearly with the number of scenarios in one of the problems, and exponentially in the other. We find that, in the former problem, maximizing the number of scenarios yields the best solution, while in the latter problem, it is necessary to evaluate multiple candidate solutions to find the best solution, since increasing the number of scenarios becomes expensive very quickly.