357 research outputs found
Hypothesis Testing Interpretations and Renyi Differential Privacy
Differential privacy is a de facto standard in data privacy, with
applications in the public and private sectors. A way to explain differential
privacy, which is particularly appealing to statistician and social scientists
is by means of its statistical hypothesis testing interpretation. Informally,
one cannot effectively test whether a specific individual has contributed her
data by observing the output of a private mechanism---any test cannot have both
high significance and high power.
In this paper, we identify some conditions under which a privacy definition
given in terms of a statistical divergence satisfies a similar interpretation.
These conditions are useful to analyze the distinguishability power of
divergences and we use them to study the hypothesis testing interpretation of
some relaxations of differential privacy based on Renyi divergence. This
analysis also results in an improved conversion rule between these definitions
and differential privacy
Synthesizing Probabilistic Invariants via Doob's Decomposition
When analyzing probabilistic computations, a powerful approach is to first
find a martingale---an expression on the program variables whose expectation
remains invariant---and then apply the optional stopping theorem in order to
infer properties at termination time. One of the main challenges, then, is to
systematically find martingales.
We propose a novel procedure to synthesize martingale expressions from an
arbitrary initial expression. Contrary to state-of-the-art approaches, we do
not rely on constraint solving. Instead, we use a symbolic construction based
on Doob's decomposition. This procedure can produce very complex martingales,
expressed in terms of conditional expectations.
We show how to automatically generate and simplify these martingales, as well
as how to apply the optional stopping theorem to infer properties at
termination time. This last step typically involves some simplification steps,
and is usually done manually in current approaches. We implement our techniques
in a prototype tool and demonstrate our process on several classical examples.
Some of them go beyond the capability of current semi-automatic approaches
Proving uniformity and independence by self-composition and coupling
Proof by coupling is a classical proof technique for establishing
probabilistic properties of two probabilistic processes, like stochastic
dominance and rapid mixing of Markov chains. More recently, couplings have been
investigated as a useful abstraction for formal reasoning about relational
properties of probabilistic programs, in particular for modeling
reduction-based cryptographic proofs and for verifying differential privacy. In
this paper, we demonstrate that probabilistic couplings can be used for
verifying non-relational probabilistic properties. Specifically, we show that
the program logic pRHL---whose proofs are formal versions of proofs by
coupling---can be used for formalizing uniformity and probabilistic
independence. We formally verify our main examples using the EasyCrypt proof
assistant
A Relational Logic for Higher-Order Programs
Relational program verification is a variant of program verification where
one can reason about two programs and as a special case about two executions of
a single program on different inputs. Relational program verification can be
used for reasoning about a broad range of properties, including equivalence and
refinement, and specialized notions such as continuity, information flow
security or relative cost. In a higher-order setting, relational program
verification can be achieved using relational refinement type systems, a form
of refinement types where assertions have a relational interpretation.
Relational refinement type systems excel at relating structurally equivalent
terms but provide limited support for relating terms with very different
structures.
We present a logic, called Relational Higher Order Logic (RHOL), for proving
relational properties of a simply typed -calculus with inductive types
and recursive definitions. RHOL retains the type-directed flavour of relational
refinement type systems but achieves greater expressivity through rules which
simultaneously reason about the two terms as well as rules which only
contemplate one of the two terms. We show that RHOL has strong foundations, by
proving an equivalence with higher-order logic (HOL), and leverage this
equivalence to derive key meta-theoretical properties: subject reduction,
admissibility of a transitivity rule and set-theoretical soundness. Moreover,
we define sound embeddings for several existing relational type systems such as
relational refinement types and type systems for dependency analysis and
relative cost, and we verify examples that were out of reach of prior work.Comment: Submitted to ICFP 201
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