DR.SGX: Hardening SGX Enclaves against Cache Attacks with Data Location Randomization

Recent research has demonstrated that Intel's SGX is vulnerable to various software-based side-channel attacks. In particular, attacks that monitor CPU caches shared between the victim enclave and untrusted software enable accurate leakage of secret enclave data. Known defenses assume developer assistance, require hardware changes, impose high overhead, or prevent only some of the known attacks. In this paper we propose data location randomization as a novel defensive approach to address the threat of side-channel attacks. Our main goal is to break the link between the cache observations by the privileged adversary and the actual data accesses by the victim. We design and implement a compiler-based tool called DR.SGX that instruments enclave code such that data locations are permuted at the granularity of cache lines. We realize the permutation with the CPU's cryptographic hardware-acceleration units providing secure randomization. To prevent correlation of repeated memory accesses we continuously re-randomize all enclave data during execution. Our solution effectively protects many (but not all) enclaves from cache attacks and provides a complementary enclave hardening technique that is especially useful against unpredictable information leakage

Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection

The effective detection of evidence of financial anomalies requires collaboration among multiple entities who own a diverse set of data, such as a payment network system (PNS) and its partner banks. Trust among these financial institutions is limited by regulation and competition. Federated learning (FL) enables entities to collaboratively train a model when data is either vertically or horizontally partitioned across the entities. However, in real-world financial anomaly detection scenarios, the data is partitioned both vertically and horizontally and hence it is not possible to use existing FL approaches in a plug-and-play manner. Our novel solution, PV4FAD, combines fully homomorphic encryption (HE), secure multi-party computation (SMPC), differential privacy (DP), and randomization techniques to balance privacy and accuracy during training and to prevent inference threats at model deployment time. Our solution provides input privacy through HE and SMPC, and output privacy against inference time attacks through DP. Specifically, we show that, in the honest-but-curious threat model, banks do not learn any sensitive features about PNS transactions, and the PNS does not learn any information about the banks' dataset but only learns prediction labels. We also develop and analyze a DP mechanism to protect output privacy during inference. Our solution generates high-utility models by significantly reducing the per-bank noise level while satisfying distributed DP. To ensure high accuracy, our approach produces an ensemble model, in particular, a random forest. This enables us to take advantage of the well-known properties of ensembles to reduce variance and increase accuracy. Our solution won second prize in the first phase of the U.S. Privacy Enhancing Technologies (PETs) Prize Challenge.Comment: Prize Winner in the U.S. Privacy Enhancing Technologies (PETs) Prize Challeng

The Long Road to Computational Location Privacy: A Survey

The widespread adoption of continuously connected smartphones and tablets developed the usage of mobile applications, among which many use location to provide geolocated services. These services provide new prospects for users: getting directions to work in the morning, leaving a check-in at a restaurant at noon and checking next day's weather in the evening are possible right from any mobile device embedding a GPS chip. In these location-based applications, the user's location is sent to a server, which uses them to provide contextual and personalised answers. However, nothing prevents the latter from gathering, analysing and possibly sharing the collected information, which opens the door to many privacy threats. Indeed, mobility data can reveal sensitive information about users, among which one's home, work place or even religious and political preferences. For this reason, many privacy-preserving mechanisms have been proposed these last years to enhance location privacy while using geolocated services. This article surveys and organises contributions in this area from classical building blocks to the most recent developments of privacy threats and location privacy-preserving mechanisms. We divide the protection mechanisms between online and offline use cases, and organise them into six categories depending on the nature of their algorithm. Moreover, this article surveys the evaluation metrics used to assess protection mechanisms in terms of privacy, utility and performance. Finally, open challenges and new directions to address the problem of computational location privacy are pointed out and discussed

Performance evaluation of cooperation strategies for m-health services and applications

Health telematics are becoming a major improvement for patients’ lives, especially for disabled, elderly, and chronically ill people. Information and communication technologies have rapidly grown along with the mobile Internet concept of anywhere and anytime connection. In this context, Mobile Health (m-Health) proposes healthcare services delivering, overcoming geographical, temporal and even organizational barriers. Pervasive and m-Health services aim to respond several emerging problems in health services, including the increasing number of chronic diseases related to lifestyle, high costs in existing national health services, the need to empower patients and families to self-care and manage their own healthcare, and the need to provide direct access to health services, regardless the time and place. Mobile Health (m- Health) systems include the use of mobile devices and applications that interact with patients and caretakers. However, mobile devices have several constraints (such as, processor, energy, and storage resource limitations), affecting the quality of service and user experience. Architectures based on mobile devices and wireless communications presents several challenged issues and constraints, such as, battery and storage capacity, broadcast constraints, interferences, disconnections, noises, limited bandwidths, and network delays. In this sense, cooperation-based approaches are presented as a solution to solve such limitations, focusing on increasing network connectivity, communication rates, and reliability. Cooperation is an important research topic that has been growing in recent years. With the advent of wireless networks, several recent studies present cooperation mechanisms and algorithms as a solution to improve wireless networks performance. In the absence of a stable network infrastructure, mobile nodes cooperate with each other performing all networking functionalities. For example, it can support intermediate nodes forwarding packets between two distant nodes. This Thesis proposes a novel cooperation strategy for m-Health services and applications. This reputation-based scheme uses a Web-service to handle all the nodes reputation and networking permissions. Its main goal is to provide Internet services to mobile devices without network connectivity through cooperation with neighbor devices. Therefore resolving the above mentioned network problems and resulting in a major improvement for m-Health network architectures performances. A performance evaluation of this proposal through a real network scenario demonstrating and validating this cooperative scheme using a real m-Health application is presented. A cryptography solution for m-Health applications under cooperative environments, called DE4MHA, is also proposed and evaluated using the same real network scenario and the same m-Health application. Finally, this work proposes, a generalized cooperative application framework, called MobiCoop, that extends the incentive-based cooperative scheme for m-Health applications for all mobile applications. Its performance evaluation is also presented through a real network scenario demonstrating and validating MobiCoop using different mobile applications

Framework for privacy-aware content distribution in peer-to- peer networks with copyright protection

The use of peer-to-peer (P2P) networks for multimedia distribution has spread out globally in recent years. This mass popularity is primarily driven by the efficient distribution of content, also giving rise to piracy and copyright infringement as well as privacy concerns. An end user (buyer) of a P2P content distribution system does not want to reveal his/her identity during a transaction with a content owner (merchant), whereas the merchant does not want the buyer to further redistribute the content illegally. Therefore, there is a strong need for content distribution mechanisms over P2P networks that do not pose security and privacy threats to copyright holders and end users, respectively. However, the current systems being developed to provide copyright and privacy protection to merchants and end users employ cryptographic mechanisms, which incur high computational and communication costs, making these systems impractical for the distribution of big files, such as music albums or movies.El uso de soluciones de igual a igual (peer-to-peer, P2P) para la distribución multimedia se ha extendido mundialmente en los últimos años. La amplia popularidad de este paradigma se debe, principalmente, a la distribución eficiente de los contenidos, pero también da lugar a la piratería, a la violación del copyright y a problemas de privacidad. Un usuario final (comprador) de un sistema de distribución de contenidos P2P no quiere revelar su identidad durante una transacción con un propietario de contenidos (comerciante), mientras que el comerciante no quiere que el comprador pueda redistribuir ilegalmente el contenido más adelante. Por lo tanto, existe una fuerte necesidad de mecanismos de distribución de contenidos por medio de redes P2P que no supongan un riesgo de seguridad y privacidad a los titulares de derechos y los usuarios finales, respectivamente. Sin embargo, los sistemas actuales que se desarrollan con el propósito de proteger el copyright y la privacidad de los comerciantes y los usuarios finales emplean mecanismos de cifrado que implican unas cargas computacionales y de comunicaciones muy elevadas que convierten a estos sistemas en poco prácticos para distribuir archivos de gran tamaño, tales como álbumes de música o películas.L'ús de solucions d'igual a igual (peer-to-peer, P2P) per a la distribució multimèdia s'ha estès mundialment els darrers anys. L'àmplia popularitat d'aquest paradigma es deu, principalment, a la distribució eficient dels continguts, però també dóna lloc a la pirateria, a la violació del copyright i a problemes de privadesa. Un usuari final (comprador) d'un sistema de distribució de continguts P2P no vol revelar la seva identitat durant una transacció amb un propietari de continguts (comerciant), mentre que el comerciant no vol que el comprador pugui redistribuir il·legalment el contingut més endavant. Per tant, hi ha una gran necessitat de mecanismes de distribució de continguts per mitjà de xarxes P2P que no comportin un risc de seguretat i privadesa als titulars de drets i els usuaris finals, respectivament. Tanmateix, els sistemes actuals que es desenvolupen amb el propòsit de protegir el copyright i la privadesa dels comerciants i els usuaris finals fan servir mecanismes d'encriptació que impliquen unes càrregues computacionals i de comunicacions molt elevades que fan aquests sistemes poc pràctics per a distribuir arxius de grans dimensions, com ara àlbums de música o pel·lícules

Machine learning for smart building applications: Review and taxonomy

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field

A pattern-based framework for the design of secure and dependable SDN/NFV-enabled networks

As the world becomes an interconnected network where objects and humans interact, cyber and physical networks appear to play an important role in smart ecosystems due to their increasing use on critical infrastructure and smart cities. Software Defined Networking (SDN) and Network Function Virtualisation (NFV) are a promising combination for programmable connectivity, rapid service provisioning and service chaining as they offer the necessary end-to-end optimisations. However, with the actual exponential growth of connected devices, future networks, such as SDN and NFV, require open architectures, facilitated by standards and a strong ecosystem.In this thesis, a model-based approach is proposed to support the design and verification of secure and dependable SDN/NFV-enabled networks. The model is based on the development of a pattern-based approach to design executable patterns as solutions for reusable designs and interactions of objects, encoded in a rule based reasoning system, able to guarantee security and dependability (S&D) properties in SDN/NFV enabled networks. To execute S&D patterns, a pattern based framework is implemented for the insertion of patterns at design and at runtime level. The developed pattern framework highlights also the benefit of leveraging the flexibility of SDN/NFV-enabled networks to deploy enhanced reactive security mechanisms for the protection of the industrial network via the use of service function chaining (SFC). To prove the importance of this approach and the functionality of the pattern framework, different pattern instances are implemented to guarantee S&D in network infrastructures. The developed design patterns are able to design network topologies, guarantee network properties and offer security service provisioning and chaining. Finally, in order to evaluate the developed patterns in the pattern framework, three different use cases are described, where a number of usage scenarios are deployed and evaluated experimentally