Data produced by end devices like smartphones, sensors or IoT devices can be stored and processed across a continuum of compute resources, from end devices via fog nodes to the cloud, enabling reduced latency, increased processing speed and energy savings. However, the data may be sensitive (e.g., personal data or confidential commercially sensitive information), with regulatory or other requirements for its protection. Protecting sensitive data in the dynamic, heterogeneous, and decentralized cloud-to-edge continuum is very challenging. This paper describes a solution: FogProtect, an integrated set of four technologies to protect data in the cloud-to-edge continuum. Fog-Protect addresses four concerns: (i) control and enforcement of distributed data access and usage; (ii) management of distributed data protection policies; (iii) risk assessment for data assets in the cloud-to-edge continuum; (iv) automated optimisation and adaptation to address identified risks. FogProtect operates dynamically, reacting to system changes or detected vulnerabilities to keep the data secure across the cloud- to-edge continuum. This paper describes an overview of the FogProtect concept, discusses each of the four approaches, and illustrates their usage for the protection of data in three real-world use cases.

The last several years have given rise to an increased emphasis on the protection of personal digital data. Businesses realize the value that their data hold - if they can provide the right set of data for a specified purpose to a specified user entity according to policy. For example, the European Union's General Data Protection Regulation (GDPR) went into effect in 2018, and strictly defines rules on the access, storage and transfer of personal data.

The ability for medical professionals to efficiently process vast amounts of data is critical. We present a solution which offers cloud-secure analytics on healthcare data, utilizing FHIR, the latest standard from the HL7 organization, for the exchange of health care data, together with Apache Parquet Modular Encryption.

Building on IBM-lead work on adding encryption to Apache Parquet files, we have implemented a prototype of how an end-to-end PayHowYouDrive (PHYD) solution, offering the power of Apache Spark analytics while protecting the privacy rights of the data subjects, can be securely implemented on a public cloud.

The transition to cloud computing offers a large number of benefits, such as lower capital costs and a highly agile environment. Yet, the development of software engineering practices has not kept pace with this change. Moreover, the design and runtime behavior of cloud based services and the underlying cloud infrastructure are largely decoupled from one another.This paper describes the innovative concepts being developed by CloudWave to utilize the principles of DevOps to create an execution analytics cloud infrastructure where, through the use of programmable monitoring and online data abstraction, much more relevant information for the optimization of the ecosystem is obtained. Required optimizations are subsequently negotiated between the applications and the cloud infrastructure to obtain coordinated adaption of the ecosystem. Additionally, the project is developing the technology for a Feedback Driven Development Standard Development Kit which will utilize the data gathered through execution analytics to supply developers with a powerful mechanism to shorten application development cycles.

Java uses garbage collection (GC) for the automatic reclamation of computer memory no longer required by a running application. GC implementations for Java Virtual Machines (JVM) are typically designed for single processor machines, and do not necessarily perform well for a server program with many threads running on a multiprocessor. We designed and implemented an on-the-fly GC, based on the algorithm of Doligez, Leroy and Gonthier [13, 12] (DLG), for Java in this environment. An on-the-fly collector, a collector that does not stop the program threads, allows all processors to be utilized during collection and provides uniform response times. We extended and adapted DLG for Java (e.g., adding support for weak references) and for modern multiprocessors without sequential consistency, and added performance improvements (e.g., to keep track of the objects remaining to be traced). We compared the performance of our implementation with stop-the-world mark-sweep GC. Our measurements show th...