The decision to outsource databases is strategic in many organizations due to the increasing costs of internally managing large volumes of information. The sensitive nature of this information raises the need for powerful mechanisms to protect it against unauthorized disclosure. Centralized encryption to access control at the data owner level has been proposed as one way of handling this issue. However, its prohibitive costs renders it impractical and inflexible. A distributed cryptographic approach has been suggested as a promising alternative, where keys are distributed to users on the basis of their assigned privileges. But in this case, key management becomes problematic in the face of frequent database updates and remains an open issue. In this paper, we present a novel approach based on binary tries. By exploiting the intrinsic properties of these data structures, key management complexity, and thus its cost, is significantly reduced. Changes to the binary trie structure remain limited in the face of frequent updates. Preliminary experimental analysis demonstrates the validity and the effectiveness of our approach.

Existing data integration techniques have to be revisited to query big data collections on the Cloud. Service Level Agreements implement the contracts between the cloud provider and the users, and between the cloud and service providers. Given SLA heterogeneity and data integration scalability problems, we propose an SLA guided data integration for querying data on multiple clouds.

In the last two decades, multiple ICT evolutions boosted the ability to collect and analyze vast amounts of data (on the order of Zettabytes). Collectively, they paved the way for the so-called data economy, revolutionizing most sectors of our society, including healthcare, transportation, and grids. At the core of this revolution, distributed data-intensive applications compose services operated by multiple parties in the cloud-edge continuum; they process, manage and exchange massive amounts of data at an unprecedented rate. However, data hold little value without adequate data protection. Traditional solutions, which aim to balance data quality and protection, are insufficient to address the peculiarities of the data economy, including trustworthy data sharing and management, composite service support, and multi-party data life cycle. This article analyzes how trust management systems can regain the lead in supporting trustworthy data-intensive applications, discussing current challenges and proposing a roadmap for new-generation trust management systems in the data economy.

With the advent of sensors, more and more services are developed in order to provide customers with insights about their health and their appliances’ energy consumption at home. To do so, these services use new mining algorithms that create new inference channels. However, the collected sensor data can be diverted to infer personal data that customers do not consent to share. This indirect access to data that are not collected corresponds to inference attacks involving raw sensor data (IASD). Towards these new kinds of attacks, existing inference detection systems do not suit the representation requirements of these inference channels and of user knowledge. In this paper, we propose RICE-M (Raw sensor data based Inference ChannEl Model) that meets these inference channel representations. Based on RICE-M, we proposed RICE-Sy an extensible system able to detect IASDs, and evaluated its performance taking as a case study the MHEALTH dataset. As expected, detecting IASD is proven to be quadratic due to huge sensor data managed and a quickly growing amount of user knowledge. To overcome this drawback, we propose first a set of conceptual optimizations that reduces the detection complexity. Although becoming linear, as online detection time remains greater than a fixed acceptable query response limit, we propose two approaches to estimate the potential of RICE-Sy. The first one is based on partitioning strategies which aim at partitioning the knowledge of users. We observe that by considering the quantity of knowledge gained by a user as a partitioning criterion, the median detection time of RICE-Sy is reduced by 63%. The second approach is H-RICE-SY, a hybrid detection architecture built on RICE-Sy which limits the detection at query-time to users that have a high probability to be malicious. We show the limits of processing all malicious users at query-time, without impacting the query answer time. We observe that for a ratio of 30% users considered as malicious, the median online detection time stays under the acceptable time of 80 ms, for up to a total volume of 1.2 million user knowledge entities. Based on the observed growth rates, we have estimated that for 5% of user knowledge issued by malicious users, a maximum volume of approximately 8.6 million user’s information can be processed online in an acceptable time.