Boris Škorić

Boris Skoric, Zef Wolffs

Dirac notation is the most common way to describe quantum states and operations on states. It is very convenient and allows for quick visual distinction between vectors, scalars and operators. For quantum processes that involve interactions of multiple systems an even better visualisation has been proposed by Coecke and Kissinger, in the form of a diagrammatic formalism [CK2017]. Their notation expresses formulas in the form of diagrams, somewhat similar to Feynman diagrams, and is more general than the circuit notation for quantum computing. This document consists of two parts. (1) We give a brief summary of the diagrammatic notation of quantum processes, tailored to readers who already know quantum physics and are not interested in general process theory. For this audience our summary is less daunting than the encyclopaedic book by Coecke and Kissinger [CK2017], and on the other hand more accessible than the ultra-compact introduction of [KTW2017]. We deviate a somewhat from [CK2017,KTW2017] in that we do not assume basis states to equal their own complex conjugate; this means that we do not use symmetric notation for basis states, and it leads us to explicitly show arrows on wires where they are usually omitted. (2) We extend the work of Kissinger, Tull and Westerbaan [KTW2017] which gives a diagrammatic security proof for BB84 and 6-state Quantum Key Distribution. Their proof is based on a sequence of diagrammatic manipulations that works when the bases used in the protocol are mutually unbiased. We extend this result to 8-state encoding, which has been proposed as a tool in quantum key recycling protocols [SdV2017,LS2018], and which does not have mutually unbiased bases.

Milan Lopuhaä-Zwakenberg, Haochen Tong, Boris Škorić

In the Open Data approach, governments and other public organisations want to share their datasets with the public, for accountability and to support participation. Data must be opened in such a way that individual privacy is safeguarded. The Privacy Funnel is a mathematical approach that produces a sanitised database that does not leak private data beyond a chosen threshold. The downsides to this approach are that it does not give worst-case privacy guarantees, and that finding optimal sanitisation protocols can be computationally prohibitive. We tackle these problems by using differential privacy metrics, and by considering local protocols which operate on one entry at a time. We show that under both the Local Differential Privacy and Local Information Privacy leakage metrics, one can efficiently obtain optimal protocols. Furthermore, Local Information Privacy is both more closely aligned to the privacy requirements of the Privacy Funnel scenario, and more efficiently computable. We also consider the scenario where each user has multiple attributes, for which we define Side-channel Resistant Local Information Privacy, and we give efficient methods to find protocols satisfying this criterion while still offering good utility. Finally, we introduce Conditional Reporting, an explicit LIP protocol that can be used when the optimal protocol is infeasible to compute, and we test this protocol on real-world and synthetic data. Experiments on real-world and synthetic data confirm the validity of these methods.

Boris Skoric, Wil Michiels

We introduce a scheme for distributing and storing software with cryptographic functionality in the whitebox attacker model. Our scheme satisfies two relevant properties: incompressibility and traceability. The main idea is to store a large amount of random data (a `blob'), some of which will be randomly sampled in the future to serve as key material, and some of which serves as a watermark. We study two variants: with and without re-use of key material. For both variants we analyse how many decryptions can be performed with the blob, taking into account collusion attacks against the watermark. Our results show that application of blob schemes in the context of pay-TV is feasible.

Zitao Li, Tianhao Wang, Milan Lopuhaä-Zwakenberg et al.

When collecting information, local differential privacy (LDP) relieves the concern of privacy leakage from users' perspective, as user's private information is randomized before sent to the aggregator. We study the problem of recovering the distribution over a numerical domain while satisfying LDP. While one can discretize a numerical domain and then apply the protocols developed for categorical domains, we show that taking advantage of the numerical nature of the domain results in better trade-off of privacy and utility. We introduce a new reporting mechanism, called the square wave SW mechanism, which exploits the numerical nature in reporting. We also develop an Expectation Maximization with Smoothing (EMS) algorithm, which is applied to aggregated histograms from the SW mechanism to estimate the original distributions. Extensive experiments demonstrate that our proposed approach, SW with EMS, consistently outperforms other methods in a variety of utility metrics.

Milan Lopuhaä-Zwakenberg, Zitao Li, Boris Škorić et al.

Local Differential Privacy protocols are stochastic protocols used in data aggregation when individual users do not trust the data aggregator with their private data. In such protocols there is a fundamental tradeoff between user privacy and aggregator utility. In the setting of frequency estimation, established bounds on this tradeoff are either nonquantitative, or far from what is known to be attainable. In this paper, we use information-theoretical methods to significantly improve established bounds. We also show that the new bounds are attainable for binary inputs. Furthermore, our methods lead to improved frequency estimators, which we experimentally show to outperform state-of-the-art methods.

Milan Lopuhaä-Zwakenberg, Boris Škorić, Ninghui Li

Local Differential Privacy (LDP) protocols allow an aggregator to obtain population statistics about sensitive data of a userbase, while protecting the privacy of the individual users. To understand the tradeoff between aggregator utility and user privacy, we introduce new information-theoretic metrics for utility and privacy. Contrary to other LDP metrics, these metrics highlight the fact that the users and the aggregator are interested in fundamentally different domains of information. We show how our metrics relate to $\varepsilon$-LDP, the \emph{de facto} standard privacy metric, giving an information-theoretic interpretation to the latter. Furthermore, we use our metrics to quantitatively study the privacy-utility tradeoff for a number of popular protocols.

Behrooz Razeghi, Taras Stanko, Boris Škorić et al.

We investigate the privacy of two approaches to (biometric) template protection: Helper Data Systems and Sparse Ternary Coding with Ambiguization. In particular, we focus on a privacy property that is often overlooked, namely how much leakage exists about one specific binary property of one component of the feature vector. This property is e.g. the sign or an indicator that a threshold is exceeded. We provide evidence that both approaches are able to protect such sensitive binary variables, and discuss how system parameters need to be set.

Tianhao Wang, Milan Lopuhaä-Zwakenberg, Zitao Li et al.

Local Differential Privacy (LDP) protects user privacy from the data collector. LDP protocols have been increasingly deployed in the industry. A basic building block is frequency oracle (FO) protocols, which estimate frequencies of values. While several FO protocols have been proposed, the design goal does not lead to optimal results for answering many queries. In this paper, we show that adding post-processing steps to FO protocols by exploiting the knowledge that all individual frequencies should be non-negative and they sum up to one can lead to significantly better accuracy for a wide range of tasks, including frequencies of individual values, frequencies of the most frequent values, and frequencies of subsets of values. We consider 10 different methods that exploit this knowledge differently. We establish theoretical relationships between some of them and conducted extensive experimental evaluations to understand which methods should be used for different query tasks.

Taras Stanko, Bin Chen, Boris Skoric

Storage of biometric data requires some form of template protection in order to preserve the privacy of people enrolled in a biometric database. One approach is to use a Helper Data System. Here it is necessary to transform the raw biometric measurement into a fixed-length representation. In this paper we extend the spectral function approach of Stanko and Skoric [WIFS2017], which provides such a fixed-length representation for fingerprints. First, we introduce a new spectral function that captures different information from the minutia orientations. It is complementary to the original spectral function, and we use both of them to extract information from a fingerprint image. Second, we construct a helper data system consisting of zero-leakage quantisation followed by the Code Offset Method. We show empirical data which demonstrates that applying our helper data system causes only a small performance penalty compared to fingerprint authentication based on the unprotected spectral functions.

Taras Stanko, Boris Skoric

We introduce a new fixed-length representation of fingerprint minutiae, for use in template protection. It is similar to the `spectral minutiae' representation of Xu et al. but is based on coordinate differences between pairs of minutiae. Our technique has the advantage that it does not discard the phase information of the spectral functions. We show that the fingerprint matching performance (Equal Error Rate) is comparable to that of the original spectral minutiae representation, while the speed is improved.