Jaron Skovsted Gundersen

Classical coding theory contains several techniques to obtain new codes from other codes, including puncturing and shortening. Both of these techniques have been generalized to quantum codes. Restricting to stabilizer codes, this paper introduces more freedom in the choice of the encoded states after puncturing. Furthermore, we also give an explicit description of the stabilizers for the punctured code. The additional freedom in the procedure also opens up for new ways to construct new codes from old, and we present several ways to utilize this in the search for codes with good or even optimal parameters. In particular, we use the construction to obtain codes whose parameters exceed the best previously known and which are better than what general puncturing guarantees. Lastly, the freedom in our puncture procedure allowed us to generalize the proof of the Griesmer bound from the classical setting to stabilizer codes for qudits of prime dimension since the proof relies heavily on the puncturing technique.

Privacy-preserving distributed processing has recently attracted considerable attention. It aims to design solutions for conducting signal processing tasks over networks in a decentralized fashion without violating privacy. Many existing algorithms can be adopted to solve this problem such as differential privacy, secure multiparty computation, and the recently proposed distributed optimization based subspace perturbation algorithms. However, since each of them is derived from a different context and has different metrics and assumptions, it is hard to choose or design an appropriate algorithm in the context of distributed processing. In order to address this problem, we first propose general mutual information based information-theoretical metrics that are able to compare and relate these existing algorithms in terms of two key aspects: output utility and individual privacy. We consider two widely-used adversary models, the passive and eavesdropping adversary. Moreover, we derive a lower bound on individual privacy which helps to understand the nature of the problem and provides insights on which algorithm is preferred given different conditions. To validate the above claims, we investigate a concrete example and compare a number of state-of-the-art approaches in terms of the concerned aspects using not only theoretical analysis but also numerical validation. Finally, we discuss and provide principles for designing appropriate algorithms for different applications.

In this paper we consider linear secret sharing schemes over a finite field Fq, where the secret is a vector in Fℓ q and each of the n shares is a single element of Fq. We obtain lower bounds on the so-called threshold gap g of such schemes, defined as the quantity r–t where r is the smallest number such that any subset of r shares uniquely determines the secret and t is the largest number such that any subset of t shares provides no information about the secret. Our main result establishes a family of bounds which are tighter than previously known bounds for ℓ ≥ 2. Furthermore, we also provide bounds, in terms of n and q, on the partial reconstruction and privacy thresholds, a more finegrained notion that considers the amount of information about the secret that can be contained in a set of shares of a given size. Finally, we compare our lower bounds with known upper bounds in the asymptotic setting.