Probabilistic Opacity for Markov Decision Processes
نویسندگان
چکیده
Opacity is a generic security property, that has been defined on (non probabilistic) transition systems and later on Markov chains with labels. For a secret predicate, given as a subset of runs, and a function describing the view of an external observer, the value of interest for opacity is a measure of the set of runs disclosing the secret. We extend this definition to the richer framework of Markov decision processes, where non deterministic choice is combined with probabilistic transitions, and we study related decidability problems with partial or complete observation hypotheses for the schedulers. We prove that all questions are decidable with complete observation and ω-regular secrets. With partial observation, we prove that all quantitative questions are undecidable but the question whether a system is almost surely non opaque becomes decidable for a restricted class of ω-regular secrets, as well as for all ω-regular secrets under finite-memory schedulers.
منابع مشابه
Probabilistic Sufficiency and Algorithmic Sufficiency from the point of view of Information Theory
Given the importance of Markov chains in information theory, the definition of conditional probability for these random processes can also be defined in terms of mutual information. In this paper, the relationship between the concept of sufficiency and Markov chains from the perspective of information theory and the relationship between probabilistic sufficiency and algorithmic sufficien...
متن کاملA Markov Model for Performance Evaluation of Coal Handling Unit of a Thermal Power Plant
The present paper discusses the development of a Markov model for performance evaluation of coal handling unit of a thermal power plant using probabilistic approach. Coal handling unit ensures proper supply of coal for sound functioning of thermal Power Plant. In present paper, the coal handling unit consists of two subsystems with two possible states i.e. working and failed. Failure and repair...
متن کاملAccelerated decomposition techniques for large discounted Markov decision processes
Many hierarchical techniques to solve large Markov decision processes (MDPs) are based on the partition of the state space into strongly connected components (SCCs) that can be classified into some levels. In each level, smaller problems named restricted MDPs are solved, and then these partial solutions are combined to obtain the global solution. In this paper, we first propose a novel algorith...
متن کاملOn the Undecidability of Probabilistic Planning and Infinite-Horizon Partially Observable Markov Decision Problems
We investigate the computability of problems in probabilistic planning and partially observable infinite-horizon Markov decision processes. The undecidability of the string-existence problem for probabilistic finite automata is adapted to show that the following problem of plan existence in probabilistic planning is undecidable: given a probabilistic planning problem, determine whether there ex...
متن کاملOn the Undecidability of Probabilistic Planning and Innnite-horizon Partially Observable Markov Decision Problems
We investigate the computability of problems in probabilistic planning and partially observable innnite-horizon Markov decision processes. The undecidability of the string-existence problem for probabilistic nite automata is adapted to show that the following problem of plan existence in probabilistic planning is undecidable: given a probabilistic planning problem, determine whether there exist...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Inf. Process. Lett.
دوره 115 شماره
صفحات -
تاریخ انتشار 2015