Implementing Homomorphic Encryption Based Secure Feedback Control for Physical Systems

This paper is about an encryption based approach to the secure implementation of feedback controllers for physical systems. Specifically, Paillier's homomorphic encryption is used to digitally implement a class of linear dynamic controllers, which includes the commonplace static gain and PID type feedback control laws as special cases. The developed implementation is amenable to Field Programmable Gate Array (FPGA) realization. Experimental results, including timing analysis and resource usage characteristics for different encryption key lengths, are presented for the realization of an inverted pendulum controller; as this is an unstable plant, the control is necessarily fast

A Trust-based Recommender System over Arbitrarily Partitioned Data with Privacy

Recommender systems are effective mechanisms for recommendations about what to watch, read, or taste based on user ratings about experienced products or services. To achieve higher quality recommendations, e-commerce parties may prefer to collaborate over partitioned data. Due to privacy issues, they might hesitate to work in pairs and some solutions motivate them to collaborate. This study examines how to estimate trust-based predictions on arbitrarily partitioned data in which two parties have ratings for similar sets of customers and items. A privacy- preserving scheme is proposed, and it is justified that it efficiently offers trust-based predictions on partitioned data while preserving privacy

Encrypted control for networked systems -- An illustrative introduction and current challenges

Cloud computing and distributed computing are becoming ubiquitous in many modern control systems such as smart grids, building automation, robot swarms or intelligent transportation systems. Compared to "isolated" control systems, the advantages of cloud-based and distributed control systems are, in particular, resource pooling and outsourcing, rapid scalability, and high performance. However, these capabilities do not come without risks. In fact, the involved communication and processing of sensitive data via public networks and on third-party platforms promote, among other cyberthreats, eavesdropping and manipulation of data. Encrypted control addresses this security gap and provides confidentiality of the processed data in the entire control loop. This paper presents a tutorial-style introduction to this young but emerging field in the framework of secure control for networked dynamical systems.Comment: The paper is a preprint of an accepted paper in the IEEE Control Systems Magazin

Squirrel: A Scalable Secure Two-Party Computation Framework for Training Gradient Boosting Decision Tree

Gradient Boosting Decision Tree (GBDT) and its variants are widely used in industry, due to their strong interpretability. Secure multi-party computation allows multiple data owners to compute a function jointly while keeping their input private. In this work, we present Squirrel, a two-party GBDT training framework on a vertically split dataset, where two data owners each hold different features of the same data samples. Squirrel is private against semi-honest adversaries, and no sensitive intermediate information is revealed during the training process. Squirrel is also scalable to datasets with millions of samples even under a Wide Area Network (WAN). Squirrel achieves its high performance via several novel co-designs of the GBDT algorithms and advanced cryptography. Especially, 1) we propose a new and efficient mechanism to hide the sample distribution on each node using oblivious transfer. 2) We propose a highly optimized method for gradient aggregation using lattice-based homomorphic encryption (HE). Our empirical results show that our method can be three orders of magnitude faster than the existing HE approaches. 3) We propose a novel protocol to evaluate the sigmoid func- tion on secretly shared values, showing 19×-200×-fold im- provements over two existing methods. Combining all these improvements, Squirrel costs less than 6 seconds per tree on a dataset with 50 thousands samples which outperforms Pivot (VLDB 2020) by more than 28×. We also show that Squirrel can scale up to datasets with more than one million samples, e.g., about 170 seconds per tree over a WAN

Toward practical and private online services

Today's common online services (social networks, media streaming, messaging, email, etc.) bring convenience. However, these services are susceptible to privacy leaks. Certainly, email snooping by rogue employees, email server hacks, and accidental disclosures of user ratings for movies are some sources of private information leakage. This dissertation investigates the following question: Can we build systems that (a) provide strong privacy guarantees to the users, (b) are consistent with existing commercial and policy regimes, and (c) are affordable? Satisfying all three requirements simultaneously is challenging, as providing strong privacy guarantees usually necessitates either sacrificing functionality, incurring high resource costs, or both. Indeed, there are powerful cryptographic protocols---private information retrieval (PIR), and secure two-party computation (2PC)---that provide strong guarantees but are orders of magnitude more expensive than their non-private counterparts. This dissertation takes these protocols as a starting point and then substantially reduces their costs by tailoring them using application-specific properties. It presents two systems, Popcorn and Pretzel, built on this design ethos. Popcorn is a Netflix-like media delivery system, that provably hides, even from the content distributor (for example, Netflix), which movie a user is watching. Popcorn tailors PIR protocols to the media domain. It amortizes the server-side overhead of PIR by batching requests from the large number of concurrent users retrieving content at any given time; and, it forms large batches without introducing playback delays by leveraging the properties of media streaming. Popcorn is consistent with the prevailing commercial regime (copyrights, etc.), and its per-request dollar cost is 3.87 times that of a non-private system. The other system described in this dissertation, Pretzel, is an email system that encrypts emails end-to-end between senders and intended recipients, but allows the email service provider to perform content-based spam filtering and targeted advertising. Pretzel refines a 2PC protocol. It reduces the resource consumption of the protocol by replacing the underlying encryption scheme with a more efficient one, applying a packing technique to conserve invocations of the encryption algorithm, and pruning the inputs to the protocol. Pretzel's costs, versus a legacy non-private implementation, are estimated to be up to 5.4 times for the email provider, with additional but modest client-side requirements. Popcorn and Pretzel have fundamental connections. For instance, the cryptographic protocols in both systems securely compute vector-matrix products. However, we observe that differences in the vector and matrix dimensions lead to different system designs. Ultimately, both systems represent a potentially appealing compromise: sacrifice some functionality to build in strong privacy properties at affordable costs.Computer Science

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

Systematic Literature Review of EM-SCA Attacks on Encryption

Cryptography is vital for data security, but cryptographic algorithms can still be vulnerable to side-channel attacks (SCAs), physical assaults exploiting power consumption and EM radiation. SCAs pose a significant threat to cryptographic integrity, compromising device keys. While literature on SCAs focuses on real-world devices, the rise of sophisticated devices necessitates fresh approaches. Electromagnetic side-channel analysis (EM-SCA) gathers information by monitoring EM radiation, capable of retrieving encryption keys and detecting malicious activity. This study evaluates EM-SCA's impact on encryption across scenarios and explores its role in digital forensics and law enforcement. Addressing encryption susceptibility to EM-SCA can empower forensic investigators in overcoming encryption challenges, maintaining their crucial role in law enforcement. Additionally, the paper defines EM-SCA's current state in attacking encryption, highlighting vulnerable and resistant encryption algorithms and devices, and promising EM-SCA approaches. This study offers a comprehensive analysis of EM-SCA in law enforcement and digital forensics, suggesting avenues for further research

Actas de la XIII Reunión Española sobre Criptología y Seguridad de la Información RECSI XIII : Alicante, 2-5 de septiembre de 2014

Si tuviéramos que elegir un conjunto de palabras clave para definir la sociedad actual, sin duda el término información sería uno de los más representativos. Vivimos en un mundo caracterizado por un continuo flujo de información en el que las Tecnologías de la Información y Comunicación (TIC) y las Redes Sociales desempeñan un papel relevante. En la Sociedad de la Información se generan gran variedad de datos en formato digital, siendo la protección de los mismos frente a accesos y usos no autorizados el objetivo principal de lo que conocemos como Seguridad de la Información. Si bien la Criptología es una herramienta tecnológica básica, dedicada al desarrollo y análisis de sistemas y protocolos que garanticen la seguridad de los datos, el espectro de tecnologías que intervienen en la protección de la información es amplio y abarca diferentes disciplinas. Una de las características de esta ciencia es su rápida y constante evolución, motivada en parte por los continuos avances que se producen en el terreno de la computación, especialmente en las últimas décadas. Sistemas, protocolos y herramientas en general considerados seguros en la actualidad dejarán de serlo en un futuro más o menos cercano, lo que hace imprescindible el desarrollo de nuevas herramientas que garanticen, de forma eficiente, los necesarios niveles de seguridad. La Reunión Española sobre Criptología y Seguridad de la Información (RECSI) es el congreso científico español de referencia en el ámbito de la Criptología y la Seguridad en las TIC, en el que se dan cita periódicamente los principales investigadores españoles y de otras nacionalidades en esta disciplina, con el fin de compartir los resultados más recientes de su investigación. Del 2 al 5 de septiembre de 2014 se celebrará la decimotercera edición en la ciudad de Alicante, organizada por el grupo de Criptología y Seguridad Computacional de la Universidad de Alicante. Las anteriores ediciones tuvieron lugar en Palma de Mallorca (1991), Madrid (1992), Barcelona (1994), Valladolid (1996), Torremolinos (1998), Santa Cruz de Tenerife (2000), Oviedo (2002), Leganés (2004), Barcelona (2006), Salamanca (2008), Tarragona (2010) y San Sebastián (2012)

Vers une arithmétique efficace pour le chiffrement homomorphe basé sur le Ring-LWE

Fully homomorphic encryption is a kind of encryption offering the ability to manipulate encrypted data directly through their ciphertexts. In this way it is possible to process sensitive data without having to decrypt them beforehand, ensuring therefore the datas' confidentiality. At the numeric and cloud computing era this kind of encryption has the potential to considerably enhance privacy protection. However, because of its recent discovery by Gentry in 2009, we do not have enough hindsight about it yet. Therefore several uncertainties remain, in particular concerning its security and efficiency in practice, and should be clarified before an eventual widespread use. This thesis deals with this issue and focus on performance enhancement of this kind of encryption in practice. In this perspective we have been interested in the optimization of the arithmetic used by these schemes, either the arithmetic underlying the Ring Learning With Errors problem on which the security of these schemes is based on, or the arithmetic specific to the computations required by the procedures of some of these schemes. We have also considered the optimization of the computations required by some specific applications of homomorphic encryption, and in particular for the classification of private data, and we propose methods and innovative technics in order to perform these computations efficiently. We illustrate the efficiency of our different methods through different software implementations and comparisons to the related art.Le chiffrement totalement homomorphe est un type de chiffrement qui permet de manipuler directement des données chiffrées. De cette manière, il est possible de traiter des données sensibles sans avoir à les déchiffrer au préalable, permettant ainsi de préserver la confidentialité des données traitées. À l'époque du numérique à outrance et du "cloud computing" ce genre de chiffrement a le potentiel pour impacter considérablement la protection de la vie privée. Cependant, du fait de sa découverte récente par Gentry en 2009, nous manquons encore de recul à son propos. C'est pourquoi de nombreuses incertitudes demeurent, notamment concernant sa sécurité et son efficacité en pratique, et devront être éclaircies avant une éventuelle utilisation à large échelle.Cette thèse s'inscrit dans cette problématique et se concentre sur l'amélioration des performances de ce genre de chiffrement en pratique. Pour cela nous nous sommes intéressés à l'optimisation de l'arithmétique utilisée par ces schémas, qu'elle soit sous-jacente au problème du "Ring-Learning With Errors" sur lequel la sécurité des schémas considérés est basée, ou bien spécifique aux procédures de calculs requises par certains de ces schémas. Nous considérons également l'optimisation des calculs nécessaires à certaines applications possibles du chiffrement homomorphe, et en particulier la classification de données privées, de sorte à proposer des techniques de calculs innovantes ainsi que des méthodes pour effectuer ces calculs de manière efficace. L'efficacité de nos différentes méthodes est illustrée à travers des implémentations logicielles et des comparaisons aux techniques de l'état de l'art