Juan Ramón Troncoso-Pastoriza

Privacy preserving error resilient dna searching through oblivious automata

Human Desoxyribo-Nucleic Acid (DNA) sequences offer a wealth of information that reveal, among others, predisposition to various diseases and paternity relations. The breadth and personalized nature of this information highlights the need for privacy-preserving protocols. In this paper, we present a new error-resilient privacy-preserving string searching protocol that is suitable for running private DNA queries. This protocol checks if a short template (e.g., a string that describes a mutation leading to a disease), known to one party, is present inside a DNA sequence owned by another party, accounting for possible errors and without disclosing to each party the other partys input. Each query is formulated as a regular expression over a finite alphabet and implemented as an automaton. As the main technical contribution, we provide a protocol that allows to execute any finite state machine in an oblivious manner, requiring a communication complexity which is linear both in the number of states and the length of the input string.

Privacy-preserving data aggregation in smart metering systems: an overview

Growing energy needs are forcing governments to look for alternative resources and ways to better manage the energy grid and load balancing. As a major initiative, many countries including the United Kingdom, United States, and China have already started deploying smart grids. One of the biggest advantages of smart grids compared to traditional energy grids is the ability to remotely read fine-granular measurements from each smart meter, which enables the grid operators to balance load efficiently and offer adapted time-dependent tariffs. However, collecting fine-granular data also poses a serious privacy threat for the citizens as illustrated by the decision of the Dutch Parliament in 2009 that rejects the deployment of smart meters due to privacy considerations. Hence, it is a must to enforce privacy rights without disrupting the smart grid services like billing and data aggregation. Secure signal processing (SSP) aims at protecting the sensitive data by means of encryption and provides tools to process them under encryption, effectively addressing the smart metering privacy problem.

Watermarking security: a survey

Watermarking security has emerged in the last years as as a new subject in the watermarking area. As it brings new challenges to the design of watermarking systems, a good understanding of the problem is fundamental. This paper is intended to clarify the concepts related to watermarking security, provide an exhaustive literature overview, and serve as a starting point for newcomers interested in carrying out research on this topic.

A secure multidimensional point inclusion protocol

Signal processing in the encrypted domain combines typical signal processing operations and cryptographic primitives to ensure security in applications involving mutually distrusting participants. Several such applications reduce to a multidimensional point inclusion problem where two participants decide whether a point known to the first lies inside a region specified by the second. In a secure solution, neither party gains knowledge about the others input. For instance, in biometric authentication the client can prove his identity without disclosing his biometric. In this paper, we present a new primitive for securely solving the multidimensional point inclusion problem. Using this primitive, we first propose an efficient and provably secure protocol that solves the problem for an N-dimensional convex region bounded with hyperplanes. We subsequently extend the protocol to inclusion in multiple hyperellipsoidal regions. Considering possible reduction strategies such as input packing, we analyze the complexity of both protocols.

Secure signal processing in the cloud: enabling technologies for privacy-preserving multimedia cloud processing

In recent years, the paradigm of cloud computing has gained an increasing interest from the academic community as well as from the commercial point of view. The cloud is a very appealing concept both for the providers (who can benefit from hiring out their extra computation and storage resources) and for the users (who can avoid the initial investment on resources by outsourcing their processes and data to a cloud).

Zero-knowledge watermark detector robust to sensitivity attacks

Current zero-knowledge watermark detectors are based on a linear correlation between the asset features and a given secret sequence.This detection function is susceptible of being attacked by sensitivity attacks,for which zero-knowledge does not provide protection.In this paper a new zero-knowledge watermark detector robust to sensitivity attacks is presented,using the Generalized Gaussian Maximum Likelihood (ML)detector as basis.The inherent robustness that this detector presents against sensitivity attacks,together with the security provided by the zero-knowledge protocol that conceals the keys that could be used to remove the watermark or to produce forged assets,results in a robust and secure protocol.Additionally,two new zero-knowledge proofs for modulus and square root calculation are presented;they serve as building blocks for the zero-knowledge implementation of the Generalized Gaussian ML detector,and also open new possibilities in the design of high level protocols.

CryptoDSPs for cloud privacy

Signal processing governs almost every audiovisual stimuli that we receive from electronic sources. Recently, concerns about privacy of the processed signals (especially biomedical signals) has been raised, as it has been traditionally overlooked. This fact, together with the advent of Cloud computing and the growing tendency to outsource not only the storage but also the processing of data has created a fundamental need for privacy preserving techniques that protect signals at the Cloud. We provide a landscape of technologies brought up by the novel discipline of Signal Processing in the Encrypted Domain (SPED), and we show their application to solve Cloud Computing privacy issues, introducing the concept of virtualized CryptoDSPs, as an architecture for implementing SPED technologies on Cloud scenarios.

Secure Adaptive Filtering

In an increasingly connected world, the protection of digital data when it is processed by other parties has arisen as a major concern for the general public, and an important topic of research. The field of Signal Processing in the Encrypted Domain (SPED) has emerged in order to provide efficient and secure solutions for preserving privacy of signals that are processed by untrusted agents. In this work, we study the privacy problem of adaptive filtering, one of the most important and ubiquitous blocks in signal processing today. We present several use cases for adaptive signal processing, studying their privacy characteristics, constraints, and requirements, that differ in several aspects from those of the already tackled linear filtering and classification problems. We show the impossibility of using a strategy based solely on current homomorphic encryption systems, and we propose several novel secure protocols for a privacy-preserving execution of the least mean squares (LMS) algorithm, combining different SPED techniques, and paying special attention to the error analysis of the finite-precision implementations. We seek the best trade-offs in terms of error, computational complexity, and used bandwidth, showing a comparison among the different alternatives in these terms, and we provide the experimental results of a prototype implementation of the presented protocols, as a proof of concept that showcases the viability and efficiency of our novel solutions. The obtained results and the proposed solutions are straightforwardly extensible to other adaptive filtering algorithms, providing a basis and master guidelines for their privacy-preserving implementation.

Videosurveillance and privacy: covering the two sides of the mirror with DRM

Privacy and security have always been key concerns for individuals. They have also been closely related concepts: in order to increase their perception of security, people sacrifice a part of their privacy by accepting to be surveilled by others. The tradeoff between both is usually reasonable and commonly accepted; however, the case of videosurveillance systems has been particularly controversial since their inception, as their benefits are not perceived to compensate for the privacy loss in many cases. The situation has become even worse during the last years with the massive deployment of these systems, which often do not provide satisfactory guarantees for the citizens. This paper proposes a DRM-based framework for videosurveillance to achieve a better balance between both concepts: it protects privacy of the surveilled individuals, whilst giving support to efficient automated surveillance.

Number Theoretic Transforms for Secure Signal Processing

Multimedia contents are inherently sensitive signals that must be protected whenever they are outsourced to an untrusted environment. This problem becomes a challenge when the untrusted environment must perform some processing on the sensitive signals; a paradigmatic example is Cloud-based signal processing services. Approaches based on Secure Signal Processing (SSP) address this challenge by proposing novel mechanisms for signal processing in the encrypted domain and interactive secure protocols to achieve the goal of protecting signals without disclosing the sensitive information they convey. This paper presents a novel and comprehensive set of approaches and primitives to efficiently process signals in an encrypted form, by using Number Theoretic Transforms (NTTs) in innovative ways. This usage of NTTs paired with appropriate signal pre-and post-coding enables a whole range of easily composable signal processing operations comprising, among others, filtering, generalized convolutions, matrix-based processing or error correcting codes. Our main focus is on unattended processing, in which no interaction from the client is needed; for implementation purposes, efficient lattice-based somewhat homomorphic cryptosystems are used. We exemplify these approaches and evaluate their performance and accuracy, proving that the proposed framework opens up a wide variety of new applications for secured outsourced-processing of multimedia contents.