6 research outputs found
Bounded Expectations: Resource Analysis for Probabilistic Programs
This paper presents a new static analysis for deriving upper bounds on the
expected resource consumption of probabilistic programs. The analysis is fully
automatic and derives symbolic bounds that are multivariate polynomials of the
inputs. The new technique combines manual state-of-the-art reasoning techniques
for probabilistic programs with an effective method for automatic
resource-bound analysis of deterministic programs. It can be seen as both, an
extension of automatic amortized resource analysis (AARA) to probabilistic
programs and an automation of manual reasoning for probabilistic programs that
is based on weakest preconditions. As a result, bound inference can be reduced
to off-the-shelf LP solving in many cases and automatically-derived bounds can
be interactively extended with standard program logics if the automation fails.
Building on existing work, the soundness of the analysis is proved with respect
to an operational semantics that is based on Markov decision processes. The
effectiveness of the technique is demonstrated with a prototype implementation
that is used to automatically analyze 39 challenging probabilistic programs and
randomized algorithms. Experimental results indicate that the derived constant
factors in the bounds are very precise and even optimal for many programs
Type-Based Analysis of Logarithmic Amortised Complexity
We introduce a novel amortised resource analysis couched in a type-and-effect
system. Our analysis is formulated in terms of the physicist's method of
amortised analysis, and is potential-based. The type system makes use of
logarithmic potential functions and is the first such system to exhibit
*logarithmic amortised complexity*. With our approach we target the automated
analysis of self-adjusting data structures, like splay trees, which so far have
only manually been analysed in the literature. In particular, we have
implemented a semi-automated prototype, which successfully analyses the zig-zig
case of *splaying*, once the type annotations are fixed.Comment: 35 pages. arXiv admin note: text overlap with arXiv:1807.0824
Verifying and Synthesizing Constant-Resource Implementations with Types
We propose a novel type system for verifying that programs correctly
implement constant-resource behavior. Our type system extends recent work on
automatic amortized resource analysis (AARA), a set of techniques that
automatically derive provable upper bounds on the resource consumption of
programs. We devise new techniques that build on the potential method to
achieve compositionality, precision, and automation.
A strict global requirement that a program always maintains constant resource
usage is too restrictive for most practical applications. It is sufficient to
require that the program's resource behavior remain constant with respect to an
attacker who is only allowed to observe part of the program's state and
behavior. To account for this, our type system incorporates information flow
tracking into its resource analysis. This allows our system to certify programs
that need to violate the constant-time requirement in certain cases, as long as
doing so does not leak confidential information to attackers. We formalize this
guarantee by defining a new notion of resource-aware noninterference, and prove
that our system enforces it.
Finally, we show how our type inference algorithm can be used to synthesize a
constant-time implementation from one that cannot be verified as secure,
effectively repairing insecure programs automatically. We also show how a
second novel AARA system that computes lower bounds on resource usage can be
used to derive quantitative bounds on the amount of information that a program
leaks through its resource use. We implemented each of these systems in
Resource Aware ML, and show that it can be applied to verify constant-time
behavior in a number of applications including encryption and decryption
routines, database queries, and other resource-aware functionality.Comment: 30, IEEE S&P 201