Security protocols suite for machine-to-machine systems

Nowadays, the great diffusion of advanced devices, such as smart-phones, has shown that there is a growing trend to rely on new technologies to generate and/or support progress; the society is clearly ready to trust on next-generation communication systems to face today’s concerns on economic and social fields. The reason for this sociological change is represented by the fact that the technologies have been open to all users, even if the latter do not necessarily have a specific knowledge in this field, and therefore the introduction of new user-friendly applications has now appeared as a business opportunity and a key factor to increase the general cohesion among all citizens. Within the actors of this technological evolution, wireless machine-to-machine (M2M) networks are becoming of great importance. These wireless networks are made up of interconnected low-power devices that are able to provide a great variety of services with little or even no user intervention. Examples of these services can be fleet management, fire detection, utilities consumption (water and energy distribution, etc.) or patients monitoring. However, since any arising technology goes together with its security threats, which have to be faced, further studies are necessary to secure wireless M2M technology. In this context, main threats are those related to attacks to the services availability and to the privacy of both the subscribers’ and the services providers’ data. Taking into account the often limited resources of the M2M devices at the hardware level, ensuring the availability and privacy requirements in the range of M2M applications while minimizing the waste of valuable resources is even more challenging. Based on the above facts, this Ph. D. thesis is aimed at providing efficient security solutions for wireless M2M networks that effectively reduce energy consumption of the network while not affecting the overall security services of the system. With this goal, we first propose a coherent taxonomy of M2M network that allows us to identify which security topics deserve special attention and which entities or specific services are particularly threatened. Second, we define an efficient, secure-data aggregation scheme that is able to increase the network lifetime by optimizing the energy consumption of the devices. Third, we propose a novel physical authenticator or frame checker that minimizes the communication costs in wireless channels and that successfully faces exhaustion attacks. Fourth, we study specific aspects of typical key management schemes to provide a novel protocol which ensures the distribution of secret keys for all the cryptographic methods used in this system. Fifth, we describe the collaboration with the WAVE2M community in order to define a proper frame format actually able to support the necessary security services, including the ones that we have already proposed; WAVE2M was funded to promote the global use of an emerging wireless communication technology for ultra-low and long-range services. And finally sixth, we provide with an accurate analysis of privacy solutions that actually fit M2M-networks services’ requirements. All the analyses along this thesis are corroborated by simulations that confirm significant improvements in terms of efficiency while supporting the necessary security requirements for M2M networks

Critical Perspectives on Provable Security: Fifteen Years of Another Look Papers

We give an overview of our critiques of “proofs” of security and a guide to our papers on the subject that have appeared over the past decade and a half. We also provide numerous additional examples and a few updates and errata

A framework for implementing outsourcing schemes

En esta tesis se aborda el problema de la externalización segura de servicios de datos y computación. El escenario de interés es aquel en el que el usuario posee datos y quiere subcontratar un servidor en la nube (“Cloud”). Además, el usuario puede querer también delegar el cálculo de un subconjunto de sus datos al servidor. Se presentan dos aspectos de seguridad relacionados con este escenario, en concreto, la integridad y la privacidad y se analizan las posibles soluciones a dichas cuestiones, aprovechando herramientas criptográficas avanzadas, como el Autentificador de Mensajes Homomórfico (“Homomorphic Message Authenticators”) y el Cifrado Totalmente Homomórfico (“Fully Homomorphic Encryption”). La contribución de este trabajo es tanto teórica como práctica. Desde el punto de vista de la contribución teórica, se define un nuevo esquema de externalización (en lo siguiente, denominado con su término inglés Outsourcing), usando como punto de partida los artículos de [3] y [12], con el objetivo de realizar un modelo muy genérico y flexible que podría emplearse para representar varios esquemas de ”outsourcing” seguro. Dicho modelo puede utilizarse para representar esquemas de “outsourcing” seguro proporcionando únicamente integridad, únicamente privacidad o, curiosamente, integridad con privacidad. Utilizando este nuevo modelo también se redefine un esquema altamente eficiente, construido en [12] y que se ha denominado Outsourcinglin. Este esquema permite calcular polinomios multivariados de grado 1 sobre el anillo Z2k . Desde el punto de vista de la contribución práctica, se ha construido una infraestructura marco (“Framework”) para aplicar el esquema de “outsourcing”. Seguidamente, se ha testado dicho “Framework” con varias implementaciones, en concreto la implementación del criptosistema Joye-Libert ([18]) y la implementación del esquema propio Outsourcinglin. En el contexto de este trabajo práctico, la tesis también ha dado lugar a algunas contribuciones innovadoras: el diseño y la implementación de un nuevo algoritmo de descifrado para el esquema de cifrado Joye-Libert, en colaboración con Darío Fiore. Presenta un mejor comportamiento frente a los algoritmos propuestos por los autores de [18];la implementación de la función eficiente pseudo-aleatoria de forma amortizada cerrada (“amortized-closed-form efficient pseudorandom function”) de [12]. Esta función no se había implementado con anterioridad y no supone un problema trivial, por lo que este trabajo puede llegar a ser útil en otros contextos. Finalmente se han usado las implementaciones durante varias pruebas para medir tiempos de ejecución de los principales algoritmos.---ABSTRACT---In this thesis we tackle the problem of secure outsourcing of data and computation. The scenario we are interested in is that in which a user owns some data and wants to “outsource” it to a Cloud server. Furthermore, the user may want also to delegate the computation over a subset of its data to the server. We present the security issues related to this scenario, namely integrity and privacy and we analyse some possible solutions to these two issues, exploiting advanced cryptographic tools, such as Homomorphic Message Authenticators and Fully Homomorphic Encryption. Our contribution is both theoretical and practical. Considering our theoretical contribution, using as starting points the articles of [3] and [12], we introduce a new cryptographic primitive, called Outsourcing with the aim of realizing a very generic and flexible model that might be employed to represent several secure outsourcing schemes. Such model can be used to represent secure outsourcing schemes that provide only integrity, only privacy or, interestingly, integrity with privacy. Using our new model we also re-define an highly efficient scheme constructed in [12], that we called Outsourcinglin and that is a scheme for computing multi-variate polynomials of degree 1 over the ring Z2k. Considering our practical contribution, we build a Framework to implement the Outsourcing scheme. Then, we test such Framework to realize several implementations, specifically the implementation of the Joye-Libert cryptosystem ([18]) and the implementation of our Outsourcinglin scheme. In the context of this practical work, the thesis also led to some novel contributions: the design and the implementation, in collaboration with Dario Fiore, of a new decryption algorithm for the Joye-Libert encryption scheme, that performs better than the algorithms proposed by the authors in [18]; the implementation of the amortized-closed-form efficient pseudorandom function of [12]. There was no prior implementation of this function and it represented a non trivial work, which can become useful in other contexts. Finally we test the implementations to execute several experiments for measuring the timing performances of the main algorithms

Secure and Efficient Comparisons between Untrusted Parties

A vast number of online services is based on users contributing their personal information. Examples are manifold, including social networks, electronic commerce, sharing websites, lodging platforms, and genealogy. In all cases user privacy depends on a collective trust upon all involved intermediaries, like service providers, operators, administrators or even help desk staff. A single adversarial party in the whole chain of trust voids user privacy. Even more, the number of intermediaries is ever growing. Thus, user privacy must be preserved at every time and stage, independent of the intrinsic goals any involved party. Furthermore, next to these new services, traditional offline analytic systems are replaced by online services run in large data centers. Centralized processing of electronic medical records, genomic data or other health-related information is anticipated due to advances in medical research, better analytic results based on large amounts of medical information and lowered costs. In these scenarios privacy is of utmost concern due to the large amount of personal information contained within the centralized data. We focus on the challenge of privacy-preserving processing on genomic data, specifically comparing genomic sequences. The problem that arises is how to efficiently compare private sequences of two parties while preserving confidentiality of the compared data. It follows that the privacy of the data owner must be preserved, which means that as little information as possible must be leaked to any party participating in the comparison. Leakage can happen at several points during a comparison. The secured inputs for the comparing party might leak some information about the original input, or the output might leak information about the inputs. In the latter case, results of several comparisons can be combined to infer information about the confidential input of the party under observation. Genomic sequences serve as a use-case, but the proposed solutions are more general and can be applied to the generic field of privacy-preserving comparison of sequences. The solution should be efficient such that performing a comparison yields runtimes linear in the length of the input sequences and thus producing acceptable costs for a typical use-case. To tackle the problem of efficient, privacy-preserving sequence comparisons, we propose a framework consisting of three main parts. a) The basic protocol presents an efficient sequence comparison algorithm, which transforms a sequence into a set representation, allowing to approximate distance measures over input sequences using distance measures over sets. The sets are then represented by an efficient data structure - the Bloom filter -, which allows evaluation of certain set operations without storing the actual elements of the possibly large set. This representation yields low distortion for comparing similar sequences. Operations upon the set representation are carried out using efficient, partially homomorphic cryptographic systems for data confidentiality of the inputs. The output can be adjusted to either return the actual approximated distance or the result of an in-range check of the approximated distance. b) Building upon this efficient basic protocol we introduce the first mechanism to reduce the success of inference attacks by detecting and rejecting similar queries in a privacy-preserving way. This is achieved by generating generalized commitments for inputs. This generalization is done by treating inputs as messages received from a noise channel, upon which error-correction from coding theory is applied. This way similar inputs are defined as inputs having a hamming distance of their generalized inputs below a certain predefined threshold. We present a protocol to perform a zero-knowledge proof to assess if the generalized input is indeed a generalization of the actual input. Furthermore, we generalize a very efficient inference attack on privacy-preserving sequence comparison protocols and use it to evaluate our inference-control mechanism. c) The third part of the framework lightens the computational load of the client taking part in the comparison protocol by presenting a compression mechanism for partially homomorphic cryptographic schemes. It reduces the transmission and storage overhead induced by the semantically secure homomorphic encryption schemes, as well as encryption latency. The compression is achieved by constructing an asymmetric stream cipher such that the generated ciphertext can be converted into a ciphertext of an associated homomorphic encryption scheme without revealing any information about the plaintext. This is the first compression scheme available for partially homomorphic encryption schemes. Compression of ciphertexts of fully homomorphic encryption schemes are several orders of magnitude slower at the conversion from the transmission ciphertext to the homomorphically encrypted ciphertext. Indeed our compression scheme achieves optimal conversion performance. It further allows to generate keystreams offline and thus supports offloading to trusted devices. This way transmission-, storage- and power-efficiency is improved. We give security proofs for all relevant parts of the proposed protocols and algorithms to evaluate their security. A performance evaluation of the core components demonstrates the practicability of our proposed solutions including a theoretical analysis and practical experiments to show the accuracy as well as efficiency of approximations and probabilistic algorithms. Several variations and configurations to detect similar inputs are studied during an in-depth discussion of the inference-control mechanism. A human mitochondrial genome database is used for the practical evaluation to compare genomic sequences and detect similar inputs as described by the use-case. In summary we show that it is indeed possible to construct an efficient and privacy-preserving (genomic) sequences comparison, while being able to control the amount of information that leaves the comparison. To the best of our knowledge we also contribute to the field by proposing the first efficient privacy-preserving inference detection and control mechanism, as well as the first ciphertext compression system for partially homomorphic cryptographic systems

Biometrics & [and] Security:Combining Fingerprints, Smart Cards and Cryptography

Since the beginning of this brand new century, and especially since the 2001 Sept 11 events in the U.S, several biometric technologies are considered mature enough to be a new tool for security. Generally associated to a personal device for privacy protection, biometric references are stored in secured electronic devices such as smart cards, and systems are using cryptographic tools to communicate with the smart card and securely exchange biometric data. After a general introduction about biometrics, smart cards and cryptography, a second part will introduce our work with fake finger attacks on fingerprint sensors and tests done with different materials. The third part will present our approach for a lightweight fingerprint recognition algorithm for smart cards. The fourth part will detail security protocols used in different applications such as Personal Identity Verification cards. We will discuss our implementation such as the one we developed for the NIST to be used in PIV smart cards. Finally, a fifth part will address Cryptography-Biometrics interaction. We will highlight the antagonism between Cryptography – determinism, stable data – and Biometrics – statistical, error-prone –. Then we will present our application of challenge-response protocol to biometric data for easing the fingerprint recognition process

Survey on Fully Homomorphic Encryption, Theory, and Applications

Data privacy concerns are increasing significantly in the context of Internet of Things, cloud services, edge computing, artificial intelligence applications, and other applications enabled by next generation networks. Homomorphic Encryption addresses privacy challenges by enabling multiple operations to be performed on encrypted messages without decryption. This paper comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. The paper delves into the mathematical foundations required to understand fully homomorphic encryption (FHE). It consequently covers design fundamentals and security properties of FHE and describes the main FHE schemes based on various mathematical problems. On a more practical level, the paper presents a view on privacy-preserving Machine Learning using homomorphic encryption, then surveys FHE at length from an engineering angle, covering the potential application of FHE in fog computing, and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art FHE libraries and tools, implemented in software and hardware, and the performance thereof