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New Program Abstractions for Privacy
Static program analysis, once seen primarily as a tool for optimising programs, is now increasingly important as a means to provide quality guarantees about programs. One measure of quality is the extent to which programs respect the privacy of user data. Differential privacy is a rigorous quantified definition of privacy which guarantees a bound on the loss of privacy due to the release of statistical queries. Among the benefits enjoyed by the definition of differential privacy are compositionality properties that allow differentially private analyses to be built from pieces and combined in various ways. This has led to the development of frameworks for the construction of differentially private program analyses which are private-by-construction. Past frameworks assume that the sensitive data is collected centrally, and processed by a trusted curator. However, the main examples of differential privacy applied in practice - for example in the use of differential privacy in Google Chrome’s collection of browsing statistics, or Apple’s training of predictive messaging in iOS 10 -use a purely local mechanism applied at the data source, thus avoiding the collection of sensitive data altogether. While this is a benefit of the local approach, with systems like Apple’s, users are required to completely trust that the analysis running on their system has the claimed privacy properties.
In this position paper we outline some key challenges in developing static analyses for analysing differential privacy, and propose novel abstractions for describing the behaviour of probabilistic programs not previously used in static analyses
Privacy and Truthful Equilibrium Selection for Aggregative Games
We study a very general class of games --- multi-dimensional aggregative
games --- which in particular generalize both anonymous games and weighted
congestion games. For any such game that is also large, we solve the
equilibrium selection problem in a strong sense. In particular, we give an
efficient weak mediator: a mechanism which has only the power to listen to
reported types and provide non-binding suggested actions, such that (a) it is
an asymptotic Nash equilibrium for every player to truthfully report their type
to the mediator, and then follow its suggested action; and (b) that when
players do so, they end up coordinating on a particular asymptotic pure
strategy Nash equilibrium of the induced complete information game. In fact,
truthful reporting is an ex-post Nash equilibrium of the mediated game, so our
solution applies even in settings of incomplete information, and even when
player types are arbitrary or worst-case (i.e. not drawn from a common prior).
We achieve this by giving an efficient differentially private algorithm for
computing a Nash equilibrium in such games. The rates of convergence to
equilibrium in all of our results are inverse polynomial in the number of
players . We also apply our main results to a multi-dimensional market game.
Our results can be viewed as giving, for a rich class of games, a more robust
version of the Revelation Principle, in that we work with weaker informational
assumptions (no common prior), yet provide a stronger solution concept (ex-post
Nash versus Bayes Nash equilibrium). In comparison to previous work, our main
conceptual contribution is showing that weak mediators are a game theoretic
object that exist in a wide variety of games -- previously, they were only
known to exist in traffic routing games
Algorithms For Extracting Timeliness Graphs
We consider asynchronous message-passing systems in which some links are
timely and processes may crash. Each run defines a timeliness graph among
correct processes: (p; q) is an edge of the timeliness graph if the link from p
to q is timely (that is, there is bound on communication delays from p to q).
The main goal of this paper is to approximate this timeliness graph by graphs
having some properties (such as being trees, rings, ...). Given a family S of
graphs, for runs such that the timeliness graph contains at least one graph in
S then using an extraction algorithm, each correct process has to converge to
the same graph in S that is, in a precise sense, an approximation of the
timeliness graph of the run. For example, if the timeliness graph contains a
ring, then using an extraction algorithm, all correct processes eventually
converge to the same ring and in this ring all nodes will be correct processes
and all links will be timely. We first present a general extraction algorithm
and then a more specific extraction algorithm that is communication efficient
(i.e., eventually all the messages of the extraction algorithm use only links
of the extracted graph)
Private Multiplicative Weights Beyond Linear Queries
A wide variety of fundamental data analyses in machine learning, such as
linear and logistic regression, require minimizing a convex function defined by
the data. Since the data may contain sensitive information about individuals,
and these analyses can leak that sensitive information, it is important to be
able to solve convex minimization in a privacy-preserving way.
A series of recent results show how to accurately solve a single convex
minimization problem in a differentially private manner. However, the same data
is often analyzed repeatedly, and little is known about solving multiple convex
minimization problems with differential privacy. For simpler data analyses,
such as linear queries, there are remarkable differentially private algorithms
such as the private multiplicative weights mechanism (Hardt and Rothblum, FOCS
2010) that accurately answer exponentially many distinct queries. In this work,
we extend these results to the case of convex minimization and show how to give
accurate and differentially private solutions to *exponentially many* convex
minimization problems on a sensitive dataset
Quantitative Information Flow and Applications to Differential Privacy
International audienceSecure information flow is the problem of ensuring that the information made publicly available by a computational system does not leak information that should be kept secret. Since it is practically impossible to avoid leakage entirely, in recent years there has been a growing interest in considering the quantitative aspects of information flow, in order to measure and compare the amount of leakage. Information theory is widely regarded as a natural framework to provide firm foundations to quantitative information flow. In this notes we review the two main information-theoretic approaches that have been investigated: the one based on Shannon entropy, and the one based on Rényi min-entropy. Furthermore, we discuss some applications in the area of privacy. In particular, we consider statistical databases and the recently-proposed notion of differential privacy. Using the information-theoretic view, we discuss the bound that differential privacy induces on leakage, and the trade-off between utility and privac
Black-Box Separations for Differentially Private Protocols
We study the maximal achievable accuracy of distributed differentially private protocols for a large natural class of boolean functions, in the computational setting. In the information theoretic model, McGregor et al. [FOCS 2010] and Goyal et al. [CRYPTO 2013] have demonstrated several functionalities whose differentially private computation results in much lower accuracies in the distributed setting, as compared to the client-server setting. We explore lower bounds on the computational assumptions under which this particular accuracy gap can possibly be reduced for general two-party boolean output functions. In the distributed setting, it is possible to achieve optimal accuracy, i.e. the maximal achievable accu-racy in the client-server setting, for any function, if a semi-honest secure protocol for oblivious transfer exists. However, we show the following strong impossibility results: â—¦ For any boolean function and fixed level of privacy, the maximal achievable accuracy of any (fully) black-box construction based on existence of key-agreement protocols is at least a constant smaller than optimal achievable accuracy. Since key-agreement protocols imply the existence of one-way functions, this separation also extends to one-way functions
Order-Revealing Encryption and the Hardness of Private Learning
An order-revealing encryption scheme gives a public procedure by which two
ciphertexts can be compared to reveal the ordering of their underlying
plaintexts. We show how to use order-revealing encryption to separate
computationally efficient PAC learning from efficient -differentially private PAC learning. That is, we construct a concept
class that is efficiently PAC learnable, but for which every efficient learner
fails to be differentially private. This answers a question of Kasiviswanathan
et al. (FOCS '08, SIAM J. Comput. '11).
To prove our result, we give a generic transformation from an order-revealing
encryption scheme into one with strongly correct comparison, which enables the
consistent comparison of ciphertexts that are not obtained as the valid
encryption of any message. We believe this construction may be of independent
interest.Comment: 28 page
Computation with narrow CTCs
We examine some variants of computation with closed timelike curves (CTCs),
where various restrictions are imposed on the memory of the computer, and the
information carrying capacity and range of the CTC. We give full
characterizations of the classes of languages recognized by polynomial time
probabilistic and quantum computers that can send a single classical bit to
their own past. Such narrow CTCs are demonstrated to add the power of limited
nondeterminism to deterministic computers, and lead to exponential speedup in
constant-space probabilistic and quantum computation. We show that, given a
time machine with constant negative delay, one can implement CTC-based
computations without the need to know about the runtime beforehand.Comment: 16 pages. A few typo was correcte
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