Abstract Hidden Markov Models: a monadic account of quantitative information flow

Hidden Markov Models, HMM's, are mathematical models of Markov processes with state that is hidden, but from which information can leak. They are typically represented as 3-way joint-probability distributions. We use HMM's as denotations of probabilistic hidden-state sequential programs: for that, we recast them as `abstract' HMM's, computations in the Giry monad $\mathbb{D}$, and we equip them with a partial order of increasing security. However to encode the monadic type with hiding over some state $\mathcal{X}$ we use $\mathbb{D}\mathcal{X}\to \mathbb{D}^2\mathcal{X}$ rather than the conventional $\mathcal{X}{\to}\mathbb{D}\mathcal{X}$ that suffices for Markov models whose state is not hidden. We illustrate the $\mathbb{D}\mathcal{X}\to \mathbb{D}^2\mathcal{X}$ construction with a small Haskell prototype. We then present uncertainty measures as a generalisation of the extant diversity of probabilistic entropies, with characteristic analytic properties for them, and show how the new entropies interact with the order of increasing security. Furthermore, we give a `backwards' uncertainty-transformer semantics for HMM's that is dual to the `forwards' abstract HMM's - it is an analogue of the duality between forwards, relational semantics and backwards, predicate-transformer semantics for imperative programs with demonic choice. Finally, we argue that, from this new denotational-semantic viewpoint, one can see that the Dalenius desideratum for statistical databases is actually an issue in compositionality. We propose a means for taking it into account

The Shadow Knows: Refinement and security in sequential programs

AbstractStepwise refinement is a crucial conceptual tool for system development, encouraging program construction via a number of separate correctness-preserving stages which ideally can be understood in isolation. A crucial conceptual component of security is an adversary’s ignorance of concealed information. We suggest a novel method of combining these two ideas.Our suggestion is based on a mathematical definition of “ignorance-preserving” refinement that extends classical refinement by limiting an adversary’s access to concealed information: moving from specification to implementation should never increase that access. The novelty is the way we achieve this in the context of sequential programs.Specifically we give an operational model (and detailed justification for it), a basic sequential programming language and its operational semantics in that model, a “logic of ignorance” interpreted over the same model, then a program-logical semantics bringing those together — and finally we use the logic to establish, via refinement, the correctness of a real (though small) protocol: Rivest’s Oblivious Transfer. A previous report⋆ treated Chaum’s Dining Cryptographers similarly.In passing we solve the Refinement Paradox for sequential programs

Hidden-Markov Program Algebra with iteration

We use Hidden Markov Models to motivate a quantitative compositional semantics for noninterference-based security with iteration, including a refinement- or "implements" relation that compares two programs with respect to their information leakage; and we propose a program algebra for source-level reasoning about such programs, in particular as a means of establishing that an "implementation" program leaks no more than its "specification" program. This joins two themes: we extend our earlier work, having iteration but only qualitative, by making it quantitative; and we extend our earlier quantitative work by including iteration. We advocate stepwise refinement and source-level program algebra, both as conceptual reasoning tools and as targets for automated assistance. A selection of algebraic laws is given to support this view in the case of quantitative noninterference; and it is demonstrated on a simple iterated password-guessing attack

A New Proof Rule for Almost-Sure Termination

An important question for a probabilistic program is whether the probability mass of all its diverging runs is zero, that is that it terminates "almost surely". Proving that can be hard, and this paper presents a new method for doing so; it is expressed in a program logic, and so applies directly to source code. The programs may contain both probabilistic- and demonic choice, and the probabilistic choices may depend on the current state. As do other researchers, we use variant functions (a.k.a. "super-martingales") that are real-valued and probabilistically might decrease on each loop iteration; but our key innovation is that the amount as well as the probability of the decrease are parametric. We prove the soundness of the new rule, indicate where its applicability goes beyond existing rules, and explain its connection to classical results on denumerable (non-demonic) Markov chains.Comment: V1 to appear in PoPL18. This version collects some existing text into new example subsection 5.5 and adds a new example 5.6 and makes further remarks about uncountable branching. The new example 5.6 relates to work on lexicographic termination methods, also to appear in PoPL18 [Agrawal et al, 2018

Characterising Testing Preorders for Finite Probabilistic Processes

In 1992 Wang & Larsen extended the may- and must preorders of De Nicola and Hennessy to processes featuring probabilistic as well as nondeterministic choice. They concluded with two problems that have remained open throughout the years, namely to find complete axiomatisations and alternative characterisations for these preorders. This paper solves both problems for finite processes with silent moves. It characterises the may preorder in terms of simulation, and the must preorder in terms of failure simulation. It also gives a characterisation of both preorders using a modal logic. Finally it axiomatises both preorders over a probabilistic version of CSP.Comment: 33 page

Who Shall Gainsay Our Decision? Choctaw Literary Nationalism in the Nineteenth Century

This study examines the writing of a group of young Choctaw intellectuals, the first generation of that society of American Indians to embrace literacy as a fully viable tool of discourse. Working in the pre-removal period, 1824-1831, as the Choctaws made preparations for their great emigration from the state of Mississippi to their new sovereign soil west of the Mississippi River, their writing evinces a nationalistic fervor. In conversation with each other, the tribal intellectuals conceptualize their transition from a pre-modern ethno-historical group to a fully-fledged constitutional republic. Primary focal texts for the study include James L. McDonald's Spectre Essay of 1830 and Peter Perkins Pitchlynn's journal of 1828. McDonald's essay presents a translation of an old Choctaw legend into English and a comparative analysis of Choctaw language arts with English language art forms. Pitchlynn's journal chronicles the findings of a multi-tribal delegation, dispatched to explore the southeastern section of Indian Territory, present-day Oklahoma, when the region was largely uninhabited and unimproved wilderness. Pitchlynn reports his encounters with such famous nineteenth century Native luminaries as Tenskwatawa, the Shawnee Prophet, and Pahuska, great chief of the Osages. Secondary texts include correspondence between McDonald, Pitchlynn and their peers in the period right after the removal Treaty of Dancing Rabbit Creek was signed, but before the emigration actually took place

The social justice issues of smoke im/mobilities

In 2014, the Hazelwood mine fire burned for 45 days. Local communities were impacted by smoke and ash, and there were reports of raised carbon monoxide levels. Local news and social media reported residents experiencing numerous physical symptoms of smoke inhalation, including bleeding noses, coughing, wheezing and chest tightness. Paper masks to filter particulate matter were made available to residents to wear outside. The dust and ash constantly seeped into homes and offices, which required cleaning daily and sometimes multiple times during the day. Smoke was free to move across physical and bodily boundaries while those most vulnerable were hampered by lack of movement: pregnant women, the elderly and children were advised to leave the area. However, this suggestion to ‘simply’ move ignored the context of a community disproportionately impacted through years of economic decline and societal change. This paper explores the unequal mobilities of smoke and people that arose as a result of this event and draws on concepts of mobility justice (Sheller 2018) and emergency mobilities (Adey 2016) to reflect on the political dimensions of uneven mobility in times of crisi

A novel analysis of utility in privacy pipelines, using Kronecker products and quantitative information flow

We combine Kronecker products, and quantitative information flow, to give a novel formal analysis for the fine-grained verification of utility in complex privacy pipelines. The combination explains a surprising anomaly in the behaviour of utility of privacy-preserving pipelines -- that sometimes a reduction in privacy results also in a decrease in utility. We use the standard measure of utility for Bayesian analysis, introduced by Ghosh at al., to produce tractable and rigorous proofs of the fine-grained statistical behaviour leading to the anomaly. More generally, we offer the prospect of formal-analysis tools for utility that complement extant formal analyses of privacy. We demonstrate our results on a number of common privacy-preserving designs

Flexible and scalable privacy assessment for very large datasets, with an application to official governmental microdata

We present a systematic refactoring of the conventional treatment of privacy analyses, basing it on mathematical concepts from the framework of Quantitative Information Flow (QIF). The approach we suggest brings three principal advantages: it is flexible, allowing for precise quantification and comparison of privacy risks for attacks both known and novel; it can be computationally tractable for very large, longitudinal datasets; and its results are explainable both to politicians and to the general public. We apply our approach to a very large case study: the Educational Censuses of Brazil, curated by the governmental agency INEP, which comprise over 90 attributes of approximately 50 million individuals released longitudinally every year since 2007. These datasets have only very recently (2018-2021) attracted legislation to regulate their privacy -- while at the same time continuing to maintain the openness that had been sought in Brazilian society. INEP's reaction to that legislation was the genesis of our project with them. In our conclusions here we share the scientific, technical, and communication lessons we learned in the process