Empowering Owners with Control in Digital Data Markets

We propose an approach for allowing data owners to trade their data in digital data market scenarios, while keeping control over them. Our solution is based on a combination of selective encryption and smart contracts deployed on a blockchain, and ensures that only authorized users who paid an agreed amount can access a data item. We propose a safe interaction protocol for regulating the interplay between a data owner and subjects wishing to purchase (a subset of) her data, and an audit process for counteracting possible misbehaviors by any of the interacting parties. Our solution aims to make a step towards the realization of data market platforms where owners can benefit from trading their data while maintaining control

Multi-Provider Secure Processing of Sensors Data

We describe the implementation of an approach for supporting secure query processing over sensors data in a multi-provider scenario. Our solution relies on the definition of authorizations regulating access to data according to three different visibility levels (no visibility, encrypted visibility, and plaintext visibility). Data processing is performed by multiple providers based on the restrictions imposed by authorizations, which may require to adjust data visibility on the fly. We describe the structure of the query optimizer and show how the operations of a computation can be assigned to different cloud providers to build an efficient, secure, and economical plan for collaborative data processing

Practical techniques building on encryption for protecting and managing data in the Cloud

Companies as well as individual users are adopting cloud solutions at an over-increasing rate for storing data and making them accessible to others. While migrating data to the cloud brings undeniable benefits in terms of data availability, scalability, and reliability, data protection is still one of the biggest concerns faced by data owners. Guaranteeing data protection means ensuring confidentiality and integrity of data and computations over them, and ensuring data availability to legitimate users. In this chapter, we survey some approaches for protecting data in the cloud that apply basic cryptographic techniques, possibly complementing them with additional controls, to the aim of producing efficient and effective solutions that can be used in practice

A consensus-based approach for selecting cloud plans

An important problem when moving an application to the cloud consists in selecting the most suitable cloud plan (among those available from cloud providers) for the application deployment, with the goal of finding the best match between application requirements and plan characteristics. If a user wishes to move multiple applications at the same time, this task can be complicated by the fact that different applications might have different (and possibly contrasting) requirements. In this paper, we propose an approach enabling users to select a cloud plan that best balances the satisfaction of the requirements of multiple applications. Our solution operates by first ranking the available plans for each application (matching plan characteristics and application requirements) and then by selecting, through a consensus-based process, the one that is considered more acceptable by all applications

Confidentiality Protection in Large Databases

A growing trend in today\u2019s society is outsourcing large databases to the cloud. This permits to move the management burden from the data owner to external providers, which can make vast and scalable infrastructures available at competitive prices. Since large databases can include sensitive information, effective protection of data confidentiality is a key issue to fully enable data owners to enjoy the benefits of cloud-based solutions. Data encryption and data fragmentation have been proposed as two natural solutions for protecting data confidentiality. However, their adoption does not permit to completely delegate query evaluation at the provider. In this chapter, we illustrate some encryption-based and fragmentation-based solutions for protecting data confidentiality, discussing also how they support query execution

An Authorization Model for Multi-Provider Queries

We present a novel approach for the specification and enforcement of authorizations that enables controlled data sharing for collaborative queries in the cloud. Data authorities can establish authorizations regulating access to their data distinguishing three visibility levels (no visibility, encrypted visibility, and plaintext visibility). Authorizations are enforced in the query execution by possibly restricting operation assignments to other parties and by adjusting visibility of data on-the-fly. Our approach enables users and data authorities to fully enjoy the benefits and economic savings of the competitive open cloud market, while maintaining control over data

A Fuzzy-Based Brokering Service for Cloud Plan Selection

The current cloud market features a multitude of cloud services that differ from one another in terms of functionality or of security/performance guarantees. Users wishing to use a cloud service for storing, processing, or sharing their data must be able to select the service that best matches their desiderata. In this paper, we propose a novel, user centric, brokering service for supporting users in the specification of requirements and enabling their evaluation against available cloud plans, assessing how much the different plans can satisfy the user\u2019s desiderata. Our brokering service allows users to specify their desiderata in an easy and intuitive way by using natural language expressions and high-level concepts. Fuzzy logic and fuzzy inference systems are adopted to quantitatively assess the compliance of cloud services with the users\u2019 desiderata, and hence to help users in the cloud service selection process

Supporting User Requirements and Preferences in Cloud Plan Selection

With the cloud emerging as a successful paradigm for conveniently storing, accessing, processing, and sharing information, the cloud market has seen an incredible growth. An ever-increasing number of providers offer today several cloud plans, with different guarantees in terms of service properties such as performance, cost, or security. While such a variety naturally corresponds to a diversified user demand, it is far from trivial for users to identify the cloud providers and plans that better suit their specific needs. In this paper, we address the problem of supporting users in cloud plan selection. We characterize different kinds of requirements that may need to be supported in cloud plan selection and introduce a very simple and intuitive, yet expressive, language that captures different requirements as well as preferences users may wish to express. The corresponding formal modeling permits to reason on requirements satisfaction to identify plans that meet the constraints imposed by requirements, and to produce a preference-based ranking among such plans

Loose associations to increase utility in data publishing

Data fragmentation has been proposed as a solution for protecting the confidentiality of sensitive associations when releasing data for publishing or external storage. To enrich the utility of data fragments, a recent approach has put forward the idea of complementing a pair of fragments with some (non precise, hence loose) information on the association between them. Starting from the observation that in presence of multiple fragments the publication of several independent associations between pairs of fragments can cause improper leakage of sensitive information, in this paper we extend loose associations to operate over an arbitrary number of fragments. We first illustrate how the publication of multiple loose associations between different pairs of fragments can potentially expose sensitive associations, and describe an approach for defining loose associations among an arbitrary set of fragments. We investigate how tuples in fragments can be grouped for producing loose associations so to increase the utility of queries executed over fragments. We then provide a heuristics for performing such a grouping and producing loose associations satisfying a given level of protection for sensitive associations, while achieving utility for queries over different fragments. We also illustrate the result of an extensive experimental effort over both synthetic and real datasets, which shows the efficiency and the enhanced utility provided by our proposal

Supporting Application Requirements in Cloud-based IoT Information Processing

IoT infrastructures can be seen as an interconnected network of sources of data, whose analysis and processing can be beneficial for our society. Since IoT devices are limited in storage and computation capabilities, relying on external cloud providers has recently been identified as a promising solution for storing and managing IoT data. Due to the heterogeneity of IoT data and applicative scenarios, the cloud service delivery should be driven by the requirements of the specific IoT applications. In this paper, we propose a novel approach for supporting application requirements (typically related to security, due to the inevitable concerns arising whenever data are stored and managed at external third parties) in cloud-based IoT data processing. Our solution allows a subject with an authority over an IoT infrastructure to formulate conditions that the provider must satisfy in service provisioning, and computes a SLA based on these conditions while accounting for possible dependencies among them. We also illustrate a CSP-based formulation of the problem of computing a SLA, which can be solved adopting off-the-shelves CSP solvers