Multi-party Quantum Computation

We investigate definitions of and protocols for multi-party quantum computing in the scenario where the secret data are quantum systems. We work in the quantum information-theoretic model, where no assumptions are made on the computational power of the adversary. For the slightly weaker task of verifiable quantum secret sharing, we give a protocol which tolerates any t < n/4 cheating parties (out of n). This is shown to be optimal. We use this new tool to establish that any multi-party quantum computation can be securely performed as long as the number of dishonest players is less than n/6.Comment: Masters Thesis. Based on Joint work with Claude Crepeau and Daniel Gottesman. Full version is in preparatio

Small Pseudo-Random Families of Matrices: Derandomizing Approximate Quantum Encryption

A quantum encryption scheme (also called private quantum channel, or state randomization protocol) is a one-time pad for quantum messages. If two parties share a classical random string, one of them can transmit a quantum state to the other so that an eavesdropper gets little or no information about the state being transmitted. Perfect encryption schemes leak no information at all about the message. Approximate encryption schemes leak a non-zero (though small) amount of information but require a shorter shared random key. Approximate schemes with short keys have been shown to have a number of applications in quantum cryptography and information theory. This paper provides the first deterministic, polynomial-time constructions of quantum approximate encryption schemes with short keys. Previous constructions (quant-ph/0307104) are probabilistic--that is, they show that if the operators used for encryption are chosen at random, then with high probability the resulting protocol will be a secure encryption scheme. Moreover, the resulting protocol descriptions are exponentially long. Our protocols use keys of the same length as (or better length than) the probabilistic constructions; to encrypt $n$ qubits approximately, one needs $n+o(n)$ bits of shared key. An additional contribution of this paper is a connection between classical combinatorial derandomization and constructions of pseudo-random matrix families in a continuous space.Comment: 11 pages, no figures. In Proceedings of RANDOM 2004, Cambridge, MA, August 200

A Decision-Support Framework For Using Value Capture to Fund Public Transit: Lessons From Project-Specific Analyses, Research Report 11-14

Local and state governments provide 75 percent of transit funds in the United States. With all levels of governments under significant fiscal stress, any new transit funding mechanism is welcome. Value capture (VC) is one such mechanism. Based on the “benefits received” principle, VC involves the identification and capture of public infrastructure-led increase in land value. While the literature has extensively demonstrated the property-value impacts of transit investments and has empirically simulated the potential magnitude of VC revenues for financing transit facilities, very little research has examined the suitability of VC mechanisms for specific transit projects. This report aims to fill this research gap by examining five VC mechanisms in depth: tax-increment financing (TIF), special assessment districts (SADs), transit impact fees, joint developments, and air rights. The report is intended to assist practitioners in gauging the legal, financial, and administrative suitability of VC mechanisms for meeting project-specific funding requirements

Wildlife tourism in Scotland – the example of grouse shooting

Wildlife tourism in Scotland has seen a recent increase in profile, with two reports providing new figures on the economic value of the activity. The reports, by the Scottish Government and Scottish Natural Heritage (SNH), seem likely to generate policy responses to further develop the sector

On the `Semantics' of Differential Privacy: A Bayesian Formulation

Differential privacy is a definition of "privacy'" for algorithms that analyze and publish information about statistical databases. It is often claimed that differential privacy provides guarantees against adversaries with arbitrary side information. In this paper, we provide a precise formulation of these guarantees in terms of the inferences drawn by a Bayesian adversary. We show that this formulation is satisfied by both "vanilla" differential privacy as well as a relaxation known as (epsilon,delta)-differential privacy. Our formulation follows the ideas originally due to Dwork and McSherry [Dwork 2006]. This paper is, to our knowledge, the first place such a formulation appears explicitly. The analysis of the relaxed definition is new to this paper, and provides some concrete guidance for setting parameters when using (epsilon,delta)-differential privacy.Comment: Older version of this paper was titled: "A Note on Differential Privacy: Defining Resistance to Arbitrary Side Information