Justin Thaler

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When delegating computation to a service provider, as in the cloud computing paradigm, we seek some reassurance that the output is correct and complete. Yet recomputing the output as a check is inefficient and expensive, and it may not even be feasible to store all the data locally. We are therefore interested in what can be validated by a streaming(More)
As the cloud computing paradigm has gained prominence, the need for verifiable computation has grown increasingly urgent. Protocols for verifiable computation enable a weak client to outsource difficult computations to a powerful, but untrusted server, in a way that provides the client with a guarantee that the server performed the requested computations(More)
The central goal of data stream algorithms is to process massive streams of data using <i>sublinear</i> storage space. Motivated by work in the database community on outsourcing database and data stream processing, we ask whether the space usage of such algorithms can be further reduced by enlisting a more powerful &#8220;helper&#8221; that can(More)
When computation is outsourced, the data owner would like to be assured that the desired computation has been performed correctly by the service provider. In theory, proof systems can give the necessary assurance, but prior work is not sufficiently scalable or practical. In this paper, we develop new proof protocols for verifying computations which are(More)
Several research teams have recently been working toward the development of practical general-<lb>purpose protocols for verifiable computation. These protocols enable a computationally weak verifier to<lb>offload computations to a powerful but untrusted prover, while providing the verifier with a guarantee<lb>that the prover performed the requested(More)
We study the problem of releasing k-way marginals of a database D ∈ ({0, 1}), while preserving differential privacy. The answer to a k-way marginal query is the fraction of D’s records x ∈ {0, 1} with a given value in each of a given set of up to k columns. Marginal queries enable a rich class of statistical analyses of a dataset, and designing efficient(More)
Motivated by the trend to outsource work to commercial cloud computing services, we consider a variation of the streaming paradigm where a streaming algorithm can be assisted by a powerful helper that can provide annotations to the data stream. We extend previous work on such annotation models by considering a number of graph streaming problems. Without(More)
The ε-approximate degree of a Boolean function f : {−1, 1} → {−1, 1} is the minimum degree of a real polynomial that approximates f to within ε in the `∞ norm. We prove several lower bounds on this important complexity measure by explicitly constructing solutions to the dual of an appropriate linear program. Our first result resolves the ε-approximate(More)
The Hierarchical Heavy Hitters problem extends the notion of frequent items to data arranged in a hierarchy. This problem has applications to network traffic monitoring, anomaly detection, and DDoS detection. We present a new streaming approximation algorithm for computing Hierarchical Heavy Hitters that has several advantages over previous algorithms. It(More)
We establish a generic form of hardness amplification for the approximability of constantdepth Boolean circuits by polynomials. Specifically, we show that if a Boolean circuit cannot be pointwise approximated by low-degree polynomials to within constant error in a certain onesided sense, then an OR of disjoint copies of that circuit cannot be pointwise(More)