Novel trusted execution support, as offered by Intel's Software Guard eXtensions (SGX), embeds seamlessly into user space applications by establishing regions of encrypted memory, called enclaves. Enclaves comprise code and data that is executed under special protection of the CPU and can only be accessed via an enclave defined interface. To facilitate the usability of this new system abstraction, Intel offers a software development kit (SGX SDK). While the SDK eases the use of SGX, it misses appropriate programming support for inter-enclave interaction, and demands to hardcode the exact use of trusted execution into applications, which restricts flexibility.This paper proposes EActors, an actor framework that is tailored to SGX and offers a more seamless, flexible and efficient use of trusted execution -- especially for applications demanding multiple enclaves. EActors disentangles the interaction with enclaves and, among them, from costly execution mode transitions. It features lightweight fine-grained parallelism based on the concept of actors, thereby avoiding costly SGX SDK provided synchronisation constructs. Finally, EActors offers a high degree of freedom to execute actors, either untrusted or trusted, depending on security requirements and performance demands. We implemented two use cases on top of EActors: (i) a secure instant messaging service, and (ii) a secure multi-party computation service. Both illustrate the ability of EActors to seamlessly and effectively build secure applications. Furthermore, our performance evaluation results show that securing the messaging service with EActors improves performance compared to the vanilla versions of JabberD2 and ejabberd by up to 40x.

EActors: Fast and flexible trusted computing using SGX

Recommender systems are proving to be an invaluable tool for extracting user-relevant content helping users in their daily activities (e.g., finding relevant places to visit, content to consume, items to purchase). However, to be effective, these systems need to collect and analyze large volumes of personal data (e.g., location check-ins, movie ratings, click rates .. etc.), which exposes users to numerous privacy threats. In this context, recommender systems based on Federated Learning (FL) appear to be a promising solution for enforcing privacy as they compute accurate recommendations while keeping personal data on the users' devices. However, FL, and therefore FL-based recommender systems, rely on a central server that can experience scalability issues besides being vulnerable to attacks. To remedy this, we propose PEPPER, a decentralized recommender system based on gossip learning principles. In PEPPER, users gossip model updates and aggregate them asynchronously. At the heart of PEPPER reside two key components: a personalized peer-sampling protocol that keeps in the neighborhood of each node, a proportion of nodes that have similar interests to the former and a simple yet effective model aggregation function that builds a model that is better suited to each user. Through experiments on three real datasets implementing two use cases: a location check-in recommendation and a movie recommendation, we demonstrate that our solution converges up to 42% faster than with other decentralized solutions providing up to 9% improvement on average performance metric such as hit ratio and up to 21% improvement on long tail performance compared to decentralized competitors.

pdf/pepper-empowering-user-centric-recommender-systems-over-340krere.pdf

PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning

IoT has enabled the creation of a multitude of personal applications and services for a better understanding of urban environments and our personal lives. These services are driven by the continuous collection and analysis of user data in order to provide personalized experiences. However, there is a strong need to address user privacy concerns as most of the collected data is of sensitive nature. This paper provides an overview of privacy preservation techniques and solutions proposed so far in literature along with the IoT levels at which privacy is addressed by each solution as well as their robustness to privacy breaching attacks. An analysis of functional and non-functional limitations of each solution is done, followed by a short survey of machine learning applications designed with these solutions. We identify open issues in the privacy preserving solutions when used in IoT environments. Moreover, we note that most of the privacy preservation solutions need to be adapted in the light of GDPR to accommodate the right to privacy of the users.

On the Case of Privacy in the IoT Ecosystem: A Survey

Video on Demand (VoD) streaming is the largest source of Internet traffic. Efficient and scalable VoD requires Content Delivery Networks (CDNs) whose cost are prohibitive for many providers. An alternative is to cache and serve video content using end-users devices. Direct connections between these devices complement the resources of core VoD servers with an edge-assisted collaborative CDN. VoD access histories can reveal critical personal information, and centralized VoD solutions are notorious for exploiting personal data. Hiding the interests of users from servers and edge-assisting devices is necessary for a new generation of privacy-preserving VoD services. We introduce PrivaTube, a scalable and cost-effective VoD solution. PrivaTube aggregates video content from multiple servers and edge peers to offer a high Quality of Experience (QoE) for its users. It enables privacy preservation at all levels of the content distribution process. It leverages Trusted Execution Environments (TEEs) at servers and clients, and obfuscates access patterns using fake requests that reduce the risk of personal information leaks. Fake requests are further leveraged to implement proactive provisioning and improve QoE. Our evaluation of a complete prototype shows that PrivaTube reduces the load on servers and increases QoE while providing strong privacy guarantees.

/pdf/privatube-privacy-preserving-edge-assisted-video-streaming-536078q87k.pdf

PrivaTube: Privacy-Preserving Edge-Assisted Video Streaming

Recommenders personalize the web content by typically using collaborative filtering to relate users (or items) based on explicit feedback, e.g., ratings. The difficulty of collecting this feedback has recently motivated to consider implicit feedback (e.g., item consumption along with the corresponding time). 
In this paper, we introduce the notion of consumed item pack (CIP) which enables to link users (or items) based on their implicit analogous consumption behavior. Our proposal is generic, and we show that it captures three novel implicit recommenders: a user-based (CIP-U), an item-based (CIP-I), and a word embedding-based (DEEPCIP), as well as a state-of-the-art technique using implicit feedback (FISM). We show that our recommenders handle incremental updates incorporating freshly consumed items. We demonstrate that all three recommenders provide a recommendation quality that is competitive with state-of-the-art ones, including one incorporating both explicit and implicit feedback.

Sequences, Items And Latent Links: Recommendation With Consumed Item Packs.

Recommenders widely use collaborative filtering schemes. These schemes, however, threaten privacy as user profiles are made available to the service provider hosting the recommender and can even be guessed by curious users who analyze the recommendations. Users can encrypt their profiles to hide them from the service provider and add noise to make them difficult to guess. These precautionary measures hamper latency and recommendation quality. In this paper, we present a novel recommender, X-REC, enabling an effective collaborative filtering scheme to ensure the privacy of users against the service provider (system-level privacy) or other users (user-level privacy). X-REC builds on two underlying services: X-HE, an encryption scheme designed for recommenders, and X-NN, a neighborhood selection protocol over encrypted profiles. We leverage uniform sampling to ensure differential privacy against curious users. Our extensive evaluation demonstrates that X-REC provides (1) recommendation quality similar to non-private recommenders, and (2) significant latency improvement over privacy-aware alternatives.

I Know Nothing about You But Here is What You Might Like

Enterprises increasingly move their databases into the cloud. As a result, database-as-a-service providers are challenged to meet the performance guarantees assured in service-level agreements (SLAs) while keeping hardware costs as low as possible. Being cost-effective is particularly crucial for cloud databases where the provisioned amount of DRAM dominates the hardware costs. A way to decrease the memory footprint is to leverage access skew in the workload by moving rarely accessed cold data to cheaper storage layers and retaining only frequently accessed hot data in main memory. In this paper, we present SAHARA, an advisor that proposes a table partitioning for column stores with minimal memory footprint while still adhering to all performance SLAs. SAHARA collects lightweight workload statistics, classifies data as hot and cold, and calculates optimal or near-optimal range partitioning layouts with low optimization time using a novel cost model. We integrated SAHARA into a commercial cloud database and show in our experiments for real-world and synthetic benchmarks a memory footprint reduction of 2.5 × while still fulfilling all performance SLAs provided by the customer or advertised by the DBaaS provider.

SAHARA: Memory Footprint Reduction of Cloud Databases with Automated Table Partitioning


 This paper presents a new deep learning-based parameterization approach for model calibration with two important properties: spatial adaptivity and multiresolution representation. The method aims to establish a spatially adaptive multiresolution latent space representation of subsurface property maps that enables local updates to property distributions at different scales. The deep learning model consists of a convolutional neural network architecture that learns successive mapping across multiple scales, from a coarse grid to increasingly finer grid representations. Once trained, the architecture learns latent spaces that encode spatial information across multiple scales. The resulting parameterization can facilitate the integration of data at different resolutions while enabling updates to the desired regions of the domain. Unlike the standard deep learning latent variables that are not localized and do not provide spatial adaptivity, the presented method enables local update capability that can be exploited to incorporate expert knowledge into assisted model updating workflows. Examples with two-dimensional multi-Gaussian random fields are used to introduce the method and its properties, followed by application of the method to a travel-time tomography inverse problem to investigate its model updating performance.

Deep Learning-Based Multiresolution Parameterization for Spatially Adaptive Model Updating

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I Know Nothing about You But Here is What You Might Like

SAHARA: Memory Footprint Reduction of Cloud Databases with Automated Table Partitioning

Deep Learning-Based Multiresolution Parameterization for Spatially Adaptive Model Updating