Encrypted search algorithms (ESAs) enable private search on encrypted data and can be constructed from a variety of cryptographic primitives. All knownsub-linear ESA algorithms leak information and, therefore, the design of leakage attacks is an important way to ascertain whether a given leakage profile is exploitable in practice. Recently,Oya and Kerschbaum(Usenix '22) presented an attack called IHOP that targets the query equality pattern which reveals if and when two queries are for the same keyword of a sequence of dependent queries. In this work, we continue the study of query equality leakage on dependent queries and present two new attacks in this setting which can work either as known-distribution or known-sample attacks. They model query distributions as Markov processes and leverage insights and techniques from stochastic processes and machine learning. We implement our attacks and evaluate them on real-world query logs. Our experiments show that they outperform the state-of-the-art in most settings but also have limitations inpractical settings.

An encrypted search algorithm (ESA) allows a user to encrypt its data while preserving the ability to search over it. As all practical solutions leak some information, cryptanalysis plays an important role in the area of encrypted search. Starting with the work of Islam et al. (NDSS'12), many attacks have been proposed that exploit different leakage profiles under various assumptions. While these attacks improve our understanding of leakage, it can sometimes be difficult to draw definite conclusions about their practical performance. This is due to several reasons, including a lack of open-source implementations (which are needed to reproduce results), empirical evaluations that are conducted on restricted datasets, and in some cases reliance on relatively strong assumptions that can significantly affect accuracy. In this work, we address these limitations. First, we design and implement LEAKER, an open-source framework that evaluates the major leakage attacks against any dataset and that we hope will serve the community as a common way to evaluate leakage attacks. We identify new real-world datasets that capture different use cases for ESAs and, for the first time, include real-world user queries. Finally, we use LEAKER to systematically evaluate known attacks on our datasets, uncovering sometimes unexpected properties that increase or diminish accuracy. Our evaluation shows that some attacks work better on real-world data than previously thought and that others perform worse.

AI algorithms, and machine learning (ML) techniques in particular, are increasingly important to individuals' lives, but have caused a range of privacy concerns addressed by, e.g., the European GDPR. Using cryptographic techniques, it is possible to perform inference tasks remotely on sensitive client data in a privacy-preserving way: the server learns nothing about the input data and the model predictions, while the client learns nothing about the ML model (which is often considered intellectual property and might contain traces of sensitive data). While such privacy-preserving solutions are relatively efficient, they are mostly targeted at neural networks, can degrade the predictive accuracy, and usually reveal the network's topology. Furthermore, existing solutions are not readily accessible to ML experts, as prototype implementations are not well-integrated into ML frameworks and require extensive cryptographic knowledge. In this paper, we present CryptoSPN, a framework for privacy-preserving inference of sum-product networks (SPNs). SPNs are a tractable probabilistic graphical model that allows a range of exact inference queries in linear time. Specifically, we show how to efficiently perform SPN inference via secure multi-party computation (SMPC) without accuracy degradation while hiding sensitive client and training information with provable security guarantees. Next to foundations, CryptoSPN encompasses tools to easily transform existing SPNs into privacy-preserving executables. Our empirical results demonstrate that CryptoSPN achieves highly efficient and accurate inference in the order of seconds for medium-sized SPNs.

Privacy-preserving scalar product (PPSP) protocols are an important building block for secure computation tasks in various applications. Lu et al. (TPDS'13) introduced a PPSP protocol that does not rely on cryptographic assumptions and that is used in a wide range of publications to date. In this comment paper, we show that Lu et al.'s protocol is insecure and should not be used. We describe specific attacks against it and, using impossibility results of Impagliazzo and Rudich (STOC'89), show that it is inherently insecure and cannot be fixed without relying on at least some cryptographic assumptions.

In many voice biometrics applications there is a requirement to preserve privacy, not least because of the recently enforced General Data Protection Regulation (GDPR). Though progress in bringing privacy preservation to voice biometrics is lagging behind developments in other biometrics communities, recent years have seen rapid progress, with secure computation mechanisms such as homomorphic encryption being applied successfully to speaker recognition. Even so, the computational overhead incurred by processing speech data in the encrypted domain is substantial. While still tolerable for single biometric comparisons, most state-of-the-art systems perform some form of cohort-based score normalisation, requiring many thousands of biometric comparisons. The computational overhead is then prohibitive, meaning that one must accept either degraded performance (no score normalisation) or potential for privacy violations. This paper proposes the first computationally feasible approach to privacy-preserving cohort score normalisation. Our solution is a cohort pruning scheme based on secure multi-party computation which enables privacy-preserving score normalisation using probabilistic linear discriminant analysis (PLDA) comparisons. The solution operates upon binary voice representations. While the binarisation is lossy in biometric rank-1 performance, it supports computationally-feasible biometric rank-n comparisons in the encrypted domain.

Speech recordings are a rich source of personal, sensitive data that can be used to support a plethora of diverse applications, from health profiling to biometric recognition. It is therefore essential that speech recordings are adequately protected so that they cannot be misused. Such protection, in the form of privacy-preserving technologies, is required to ensure that: (i) the biometric profiles of a given individual (e.g., across different biometric service operators) are unlinkable; (ii) leaked, encrypted biometric information is irreversible, and that (iii) biometric references are renewable. Whereas many privacy-preserving technologies have been developed for other biometric characteristics, very few solutions have been proposed to protect privacy in the case of speech signals. Despite privacy preservation this is now being mandated by recent European and international data protection regulations. With the aim of fostering progress and collaboration between researchers in the speech, biometrics and applied cryptography communities, this survey article provides an introduction to the field, starting with a legal perspective on privacy preservation in the case of speech data. It then establishes the requirements for effective privacy preservation, reviews generic cryptography-based solutions, followed by specific techniques that are applicable to speaker characterisation (biometric applications) and speech characterisation (non-biometric applications). Glancing at non-biometrics, methods are presented to avoid function creep, preventing the exploitation of biometric information, e.g., to single out an identity in speech-assisted health care via speaker characterisation. In promoting harmonised research, the article also outlines common, empirical evaluation metrics for the assessment of privacy-preserving technologies for speech data.