Many-out-of-Many Proofs and Applications to Anonymous Zether

Anonymous Zether, proposed by Bünz, Agrawal, Zamani, and Boneh (FC\u2720), is a private payment design whose wallets demand little bandwidth and need not remain online; this unique property makes it a compelling choice for resource-constrained devices. In this work, we describe an efficient construction of Anonymous Zether. Our protocol features proofs which grow only logarithmically in the size of the anonymity sets used, improving upon the linear growth attained by prior efforts. It also features competitive transaction sizes in practice (on the order of 3 kilobytes). Our central tool is a new family of extensions to Groth and Kohlweiss\u27s one-out-of-many proofs (Eurocrypt 2015), which efficiently prove statements about many messages among a list of commitments. These extensions prove knowledge of a secret subset of a public list, and assert that the commitments in the subset satisfy certain properties (expressed as linear equations). Remarkably, our communication remains logarithmic; our computation increases only by a logarithmic multiplicative factor. This technique is likely to be of independent interest. We present an open-source, Ethereum-based implementation of our Anonymous Zether construction

Optical System Identification for Passive Electro-Optical Imaging

A statistical inverse-problem approach is presented for jointly estimating camera blur from aliased data of a known calibration target. Specifically, a parametric Maximum Likelihood (ML) PSF estimate is derived for characterizing a camera's optical imperfections through the use of a calibration target in an otherwise loosely controlled environment. The unknown parameters are jointly estimated from data described by a physical forward-imaging model, and this inverse-problem approach allows one to accommodate all of the available sources of information jointly. These sources include knowledge of the forward imaging process, the types and sources of statistical uncertainty, available prior information, and the data itself. The forward model describes a broad class of imaging systems based on a parameterization with a direct mapping between its parameters and physical imaging phenomena. The imaging perspective, ambient light-levels, target-reflectance, detector gain and offset, quantum-efficiency, and read-noise levels are all treated as nuisance parameters. The Cram'{e}r-Rao Bound (CRB) is derived under this joint model, and simulations demonstrate that the proposed estimator achieves near-optimal MSE performance. Finally, the proposed method is applied to experimental data to validate both the fidelity of the forward-models, as well as to establish the utility of the resulting ML estimates for both system identification and subsequent image restoration.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153395/1/jwleblan_1.pd