Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy

By Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Pierre-Yves Strub. 2014

95 citations recorded by the source corpus.

Mechanism design is the study of algorithm design where the inputs to the algorithm are controlled by strategic agents, who must be incentivized to faithfully report them. Unlike typical programmatic properties, it is not sufficient for algorithms to merely satisfy the property, incentive properties are only useful if the strategic agents also believe this fact. Verification is an attractive way to convince agents that the incentive properties actually hold, but mechanism design poses several unique challenges: interesting properties can be sophisticated relational properties of probabilistic computations involving expected values, and mechanisms may rely on other probabilistic properties, like differential privacy, to achieve their goals. We introduce a relational refinement type system, called HOARe2, for verifying mechanism design and differential privacy. We show that HOARe2 is sound w.r.t. a denotational semantics, and correctly models (epsilon,delta)-differential privacy; moreover, we show that it subsumes DFuzz, an existing linear dependent type system for differential privacy. Finally, we develop an SMT-based implementation of HOARe2 and use it to verify challenging examples of mechanism design, including auctions and aggregative games, and new proposed examples from differential privacy.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Daniel Kifer
2. Danfeng Zhang
3. Justin Hsu
4. Guanhong Wang
5. Yuxin Wang
6. Zeyu Ding
7. Marco Gaboardi
8. Gilles Barthe

In Information Systems

1. Justin Hsu
2. Gilles Barthe
3. Aaron Roth
4. Daniel Kifer
5. Aws Albarghouthi
6. Danfeng Zhang
7. Pierre-Yves Strub

Citations in the corpus

• Advanced Probabilistic Couplings for Differential Privacy (2016, neutral): “Several tools have been proposed for providing formal verification of the differential privacy guarantee, using a wide variety of verification approaches: dynamic checking [24, 36], relational program logic [2, 6] and relational refinement type systems [9], linear (dependent) type systems [29, 42], ”
• Proving Differential Privacy via Probabilistic Couplings (2016, neutral): “…for programs, including dynamic checking [17, 26], the already mentioned relational program logic [2, 4] and relational re-finement type systems [8], linear (dependent) type systems [18, 29], product programs [5], and methods based on computing bisimu-lations families for probabilistic automata…”
• Coupling proofs are probabilistic product programs (2016, unknown): “[13] combine a relational refinement type system with a graded monad which they use for modeling differentially private computations.”
• Computer-Aided Verification for Mechanism Design (2015, methodology): “In particular, we have recently developed HOARe2, a programming language with a typechecker that can verify properties in differential privacy and mechanism design [Barthe et al. 2015].”
• Synthesizing coupling proofs of differential privacy (2017, background): “Researchers have explored a broad array of techniques for static verification of differential privacy, including linear and dependent type systems [Azevedo de Amorim et al. 2014, 2017; Barthe et al. 2015; Gaboardi et al. 2013; Reed and Pierce 2010; Zhang and Kifer 2017], relational program logics [B”
• LightDP: towards automating differential privacy proofs (2016, background): “HOARe(2) (Barthe et al. 2015) and its extension PrivInfer (Barthe et al.”
• Proving differential privacy with shadow execution (2019, result): “, [6–10]) was concerned with increasing expressiveness.”