Higher-order probabilistic adversarial computations: categorical semantics and program logics

Adversarial computations are a widely studied class of computations where resource-bounded probabilistic adversaries have access to oracles, i.e., probabilistic procedures with private state. These computations arise routinely in several domains, including security, privacy and machine learning. In this paper, we develop program logics for reasoning about adversarial computations in a higher-order setting. Our logics are built on top of a simply typed λ-calculus extended with a graded monad for probabilities and state. The grading is used to model and restrict the memory footprint and the cost (in terms of oracle calls) of computations. Under this view, an adversary is a higher-order expression that expects as arguments the code of its oracles. We develop unary program logics for reasoning about error probabilities and expected values, and a relational logic for reasoning about coupling-based properties. All logics feature rules for adversarial computations, and yield guarantees that are valid for all adversaries that satisfy a fixed resource policy. We prove the soundness of the logics in the category of quasi-Borel spaces, using a general notion of graded predicate liftings, and we use logical relations over graded predicate liftings to establish the soundness of proof rules for adversaries. We illustrate the working of our logics with simple but illustrative examples.

ReLoC Reloaded: A Mechanized Relational Logic for Fine-Grained Concurrency and Logical Atomicity

We present a new version of ReLoC: a relational separation logic for proving refinements of programs with higher-order state, fine-grained concurrency, polymorphism and recursive types. The core of ReLoC is its refinement judgment $e \precsim e' : \tau$, which states that a program $e$ refines a program $e'$ at type $\tau$. ReLoC provides type-directed structural rules and symbolic execution rules in separation-logic style for manipulating the judgment, whereas in prior work on refinements for languages with higher-order state and concurrency, such proofs were carried out by unfolding the judgment into its definition in the model. ReLoC's abstract proof rules make it simpler to carry out refinement proofs, and enable us to generalize the notion of logically atomic specifications to the relational case, which we call logically atomic relational specifications. We build ReLoC on top of the Iris framework for separation logic in Coq, allowing us to leverage features of Iris to prove soundness of ReLoC, and to carry out refinement proofs in ReLoC. We implement tactics for interactive proofs in ReLoC, allowing us to mechanize several case studies in Coq, and thereby demonstrate the practicality of ReLoC. ReLoC Reloaded extends ReLoC (LICS'18) with various technical improvements, a new Coq mechanization, and support for Iris's prophecy variables. The latter allows us to carry out refinement proofs that involve reasoning about the program's future. We also expand ReLoC's notion of logically atomic relational specifications with a new flavor based on the HOCAP pattern by Svendsen et al.

Relational Communication Spaces: Infrastructures and Discursive Practices

Digital communication technologies, social web platforms, and mobile communication have fundamentally altered the way we communicate publicly. They have also changed our perception of space, thus making a re-calibration of a spatial perspective on public communication necessary. We argue that such a new perspective must consider the relational logic of public communication, which stands in stark contrast to the plain territorial notion of space common in communication research. Conceptualising the spatiality of public communication, we draw on Löw’s (2016) sociology of space. Her relational concept of space encourages us to pay more attention to (a) the infrastructural basis of communication, (b) the operations of synthesising the relational communication space through discursive practices, and (c) power relations that determine the accessibility of public communication. Thus, focusing on infrastructures and discursive practices means highlighting crucial socio-material preconditions of public communication and considering the effects of the power relations which are inherent in their spatialisation upon the inclusivity of public communication<em>.</em> This new approach serves a dual purpose: Firstly, it works as an analytical perspective to systematically account for the spatiality of public communication. Secondly, the differentiation between infrastructural spaces and spaces of discursive practices adds explanatory value to the perspective of relational communication spaces.

The relational logic of moral inference

How do we make inferences about the moral character of others? Here we review recent work on the cognitive mechanisms of moral inference and impression updating. We show that moral inference follows basic principles of Bayesian inference, but also departs from the standard Bayesian model in ways that may facilitate the maintenance of social relationships. Moral inference is not only sensitive to whether people make moral decisions, but also to features of decisions that reveal their suitability as a relational partner. Together these findings suggest that moral inference follows a relational logic: people form and update moral impressions in ways that are responsive to the demands of ongoing social relationships and particular social roles. We discuss implications of these findings for theories of moral cognition and identify new directions for research on human morality and person perception.

Flaws in the Android Permission Protocol with Limited Verification

Purpose of the article: analysis of the resolution protocol implemented in the Android operating system as the most popular for smartphones and other electronic gadgets; consider a formal model of the Android permission protocol and describe the automatic security analysis of this model; identify potential flaws in the permitting protocol. Research method: A formal model of the Android permission protocol based on C++ using the Java NDK based on first-order relational logic is considered, with an analysis engine that performs limited model validation. Result. Created a formal model of Android permission protocol using C ++ using Java NDK. The model identified flaws in the Android permission protocol, and thus exposed Android security vulnerabilities. The developed Android protocol permission model consists of three parts: an Android device architecture query; Android permission scheme request; system operations. Fixed flaws in Android OS related to custom permissions vulnerability. An experiment is presented to demonstrate the feasibility and prevalence of custom permissions vulnerability in existing Android applications. Examination of real Android applications supports our finding that flaws in the Android permission protocol can have serious security implications for electronic gadget applications, and in some cases allows an attacker to completely bypass permission checks. A study of one of the vulnerabilities showed that it is widespread among many existing Android applications. Most developers do not perform any additional validation to ensure that inbound APIs come from trusted applications or vendors, assuming they may not be aware of a custom permissions vulnerability despite its potential for security breaches. The result will be useful for software developers for operating systems with permissions - Android, iOS and Fire OS.

Explainable and unexpectable recommendations using relational learning on multiple domains

In this research, we combine relational learning with multi-domain to develop a formal framework for a recommendation system. The design of our framework aims at: (i) constructing general rules for recommendations, (ii) providing suggested items with clear and understandable explanations, (iii) delivering a broad range of recommendations including novel and unexpected items. We use relational learning to find all possible relations, including novel relations, and to form the general rules for recommendations. Each rule is represented in relational logic, a formal language, associating with probability. The rules are used to suggest the items, in any domain, to the user whose preferences or other properties satisfy the conditions of the rule. The information described by the rule serves as an explanation for the suggested item. It states clearly why the items are chosen for the users. The explanation is in if-then logical format which is unambiguous, less redundant and more concise compared to a natural language used in other explanation recommendation systems. The explanation itself can help persuade the user to try out the suggested items, and the associated probability can drive the user to make a decision easier and faster with more confidence. Incorporating information or knowledge from multiple domains allows us to broaden our search space and provides us with more opportunities to discover items which are previously unseen or surprised to a user resulting in a wide range of recommendations. The experiment results show that our proposed algorithm is very promising. Although the quality of recommendations provided by our framework is moderate, our framework does produce interesting recommendations not found in the primitive single-domain based system and with simple and understandable explanations.

Deep Learning with Relational Logic Representations

Despite their significant success, all the existing deep neural architectures based on static computational graphs processing fixed tensor representations necessarily face fundamental limitations when presented with dynamically sized and structured data. Examples of these are sparse multi-relational structures present everywhere from biological networks and complex knowledge hyper-graphs to logical theories. Likewise, given the cryptic nature of generalization and representation learning in neural networks, potential integration with the sheer amounts of existing symbolic abstractions present in human knowledge remains highly problematic. Here, we argue that these abilities, naturally present in symbolic approaches based on the expressive power of relational logic, are necessary to be adopted for further progress of neural networks, and present a well founded learning framework for integration of deep and symbolic approaches based on the lifted modelling paradigm.