Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy)

We present a mechanism for computing asymptotically stable school optimal matchings, while guaranteeing that it is an asymptotic dominant strategy for every student to report their true preferences to the mechanism. Our main tool in this endeavor is differential privacy: we give an algorithm that coordinates a stable matching using differentially private signals, which lead to our truthfulness guarantee. This is the first setting in which it is known how to achieve nontrivial truthfulness guarantees for students when computing school optimal matchings, assuming worst- case preferences (for schools and students) in large markets

Coordination Complexity: Small Information Coordinating Large Populations

We initiate the study of a quantity that we call coordination complexity. In a distributed optimization problem, the information defining a problem instance is distributed among n parties, who need to each choose an action, which jointly will form a solution to the optimization problem. The coordination complexity represents the minimal amount of information that a centralized coordinator, who has full knowledge of the problem instance, needs to broadcast in order to coordinate the n parties to play a nearly optimal solution. We show that upper bounds on the coordination complexity of a problem imply the existence of good jointly differentially private algorithms for solving that problem, which in turn are known to upper bound the price of anarchy in certain games with dynamically changing populations. We show several results. We fully characterize the coordination complexity for the problem of computing a many-to-one matching in a bipartite graph. Our upper bound in fact extends much more generally to the problem of solving a linearly separable convex program. We also give a different upper bound technique, which we use to bound the coordination complexity of coordinating a Nash equilibrium in a routing game, and of computing a stable matching

Data Privacy Beyond Differential Privacy

Computing technologies today have made it much easier to gather personal data, ranging from GPS locations to medical records, from online behavior to social exchanges. As algorithms are constantly analyzing such detailed personal information for a wide range of computations, data privacy emerges as a paramount concern. As a strong, meaningful and rigorous notion of privacy, Differential Privacy has provided a powerful framework for designing data analysis algorithms with provable privacy guarantees. Over the past decade, there has been tremendous progress in the theory and algorithms for differential privacy, most of which consider the setting of centralized computation where a single, static database is subject to many data analyses. However, this standard framework does not capture many complex issues in modern computation. For example, the data might be distributed across self-interested agents, who may have incentive to misreport their data; and different individuals in the computation may have different expectations to privacy. The goal of this dissertation is to bring the rich theory of differential privacy to several computational problems in practice. We start by studying the problem of private counting query release for high-dimensional data, for which there are well-known computational hardness results. Despite the worst-case intractability barrier, we provide a solution with practical empirical performances by leveraging powerful optimization heuristics. Then we tackle problems within different social and economic settings, where the standard notion of differential privacy is not applicable. To that end, we use the perspective of differential privacy to design algorithms with meaningful privacy guarantees. (1) We provide privacy-preserving algorithms for solving a family of economic optimization problems under a strong relaxation of the standard definition of differential privacy---joint differential privacy. (2) We also show that (joint) differential privacy can serve as a novel tool for mechanism design when solving these optimization problems: Under our private mechanisms, the agents are incentivized to behave truthfully. (3) Finally, we consider the problem of using social network metadata to guide a search for some class of targeted individuals (for whom we cannot provide any meaningful privacy guarantees). We give a new variant of differential privacy---protected differential privacy---that guarantees differential privacy only for a subgroup of protected individuals. Under this privacy notion, we provide a family of algorithms for searching targeted individuals in the network while ensuring the privacy for the protected (un-targeted) ones

The value of data

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 71-73).Data and information are integral to the modern economic system. Advances in technology have allowed companies to both collect and utilize vast amounts of data. At times this data can be very private and collected surreptitiously. Smartphones and other devices that keep us in constant contact with the internet provide companies like Google and Facebook with a wealth of information to sell. Despite all this, there currently does not exist a systematic way to value data. In the absence of such valuations, gross economic inefficiencies are inevitable. In this thesis, we seek to model ways in which data can be bought, sold, and used fairly in an economic environment. We also develop a theory to value data in different settings. Our models and results are applied to a variety of different domains to demonstrate their efficacy. Results from game theory and mathematical programming allow us to provide fair and efficient allocations of data. This research shows that there exists an efficient and fair method with which to determine the value of information and data and to trade it fairly.by Dalton James Jones.S.M

PREFERENCE-AWARE TASK ASSIGNMENT IN MOBILE CROWDSENSING

Mobile crowdsensing (MCS) is an emerging form of crowdsourcing, which facilitates the sensing data collection with the help of mobile participants (workers). A central problem in MCS is the assignment of sensing tasks to workers. Existing work in the field mostly seek a system-level optimization of task assignments (e.g., maximize the number of completed tasks, minimize the total distance traveled by workers) without considering individual preferences of task requesters and workers. However, users may be reluctant to participate in MCS campaigns that disregard their preferences. In this dissertation, we argue that user preferences should be a primary concern in the task assignment process for an MCS campaign to be effective, and we develop preference-aware task assignment (PTA) mechanisms for five different MCS settings. Since the PTA problem is computationally hard in most of these settings, we present efficient approximation and heuristic algorithms. Extensive simulations performed on synthetic and real data sets validate our theoretical results, and demonstrate that the proposed algorithms produce near-optimal solutions in terms of preference-awareness, outperforming the state-of-the-art assignment algorithms by a wide margin in most cases

Making Decisions with Incomplete and Inaccurate Information

From assigning students to public schools to arriving at divorce settlements, there are many settings where preferences expressed by a set of stakeholders are used to make decisions that affect them. Due to its numerous applications, and thanks to the range of questions involved, such settings have received considerable attention in fields ranging from philosophy to political science, and particularly from economics and, more recently, computer science. Although there exists a significant body of literature studying such settings, much of the work in this space make the assumption that stakeholders provide complete and accurate preference information to such decision-making procedures. However, due to, say, the high cognitive burden involved or privacy concerns, this may not always be feasible. The goal of this thesis is to explicitly address these limitations. We do so by building on previous work that looks at working with incomplete information, and by introducing solution concepts and notions that support the design of algorithms and mechanisms that can handle incomplete and inaccurate information in different settings. We present our results in two parts. In Part I we look at decision-making in the presence of incomplete information. We focus on two broad themes, both from the perspective of an algorithm or mechanism designer. Informally, the first one studies the following question: Given incomplete preferences, how does one design algorithms that are `robust', i.e., ones that produce solutions that are ``good'' with respect to the underlying complete preferences? We look at this question in context of two well-studied problems, namely, i) (a version of) the two-sided matching problem and ii) (a version of) the facility location problem, and show how one can design approximately-robust algorithms in such settings. Following this, we look at the second theme, which considers the following question: Given incomplete preferences, how can one ask the agents for some more information in order to aid in the design of `robust' algorithms? We study this question in the context of the one-sided matching problem and show how even a very small amount of extra information can be used to get much better outcomes overall. In Part II we turn our attention to decision-making in the presence of inaccurate information and look at the following question: How can one design `stable' algorithms, i.e., ones that do not produce vastly different outcomes as long as there are only small inaccuracies in a stakeholder's report of their preferences? We study this in the context of fair allocation of indivisible goods among two agents and show how, in contrast to popular fair allocation algorithms, there are alternative algorithms that are fair and approximately-stable

LIPIcs, Volume 261, ICALP 2023, Complete Volume

LIPIcs, Volume 261, ICALP 2023, Complete Volum

Learning and Robustness With Applications To Mechanism Design

The design of economic mechanisms, especially auctions, is an increasingly important part of the modern economy. A particularly important property for a mechanism is strategyproofness -- the mechanism must be robust to strategic manipulations so that the participants in the mechanism have no incentive to lie. Yet in the important case when the mechanism designer's goal is to maximize their own revenue, the design of optimal strategyproof mechanisms has proved immensely difficult, with very little progress after decades of research. Recently, to escape this impasse, a number of works have parameterized auction mechanisms as deep neural networks, and used gradient descent to successfully learn approximately optimal and approximately strategyproof mechanisms. We present several improvements on these techniques. When an auction mechanism is represented as a neural network mapping bids from outcomes, strategyproofness can be thought of as a type of adversarial robustness. Making this connection explicit, we design a modified architecture for learning auctions which is amenable to integer-programming-based certification techniques from the adversarial robustness literature. Existing baselines are empirically strategyproof, but with no way to be certain how strong that guarantee really is. By contrast, we are able to provide perfectly tight bounds on the degree to which strategyproofness is violated at any given point. Existing neural networks for auctions learn to maximize revenue subject to strategyproofness. Yet in many auctions, fairness is also an important concern -- in particular, fairness with respect to the items in the auction, which may represent, for instance, ad impressions for different protected demographic groups. With our new architecture, ProportionNet, we impose fairness constraints in addition to the strategyproofness constraints, and find approximately fair, approximately optimal mechanisms which outperform baselines. With PreferenceNet, we extend this approach to notions of fairness that are learned from possibly vague human preferences. Existing network architectures can represent additive and unit-demand auctions, but are unable to imposing more complex exactly-k constraints on the allocations made to the bidders. By using the Sinkhorn algorithm to add differentiable matching constraints, we produce a network which can represent valid allocations in such settings. Finally, we present a new auction architecture which is a differentiable version of affine maximizer auctions, modified to offer lotteries in order to potentially increase revenue. This architecture is always perfectly strategyproof (avoiding the Lagrangian-based constrained optimization of RegretNet) -- to achieve this goal, however, we need to accept that we cannot in general represent the optimal auction