The demand for quickly delivering new applications is increasingly becoming a business imperative today. Application development is often done in an ad hoc manner, without standard frameworks or libraries, thus resulting in poor reuse of software assets. Web services have received much interest in industry due to their potential in facilitating seamless business-to-business or enterprise application integration. A web services composition tool can help automate the process, from creating business process functionality, to developing executable workflows, to deploying them on an execution environment. However, we find that the main approaches taken thus far to standardize and compose web services are piecemeal and insufficient. The business world has adopted a (distributed) programming approach in which web service instances are described using WSDL, composed into flows with a language like BPEL and invoked with the SOAP protocol. Academia has propounded the AI approach of formally representing web service capabilities in ontologies, and reasoning about their composition using goal-oriented inferencing techniques from planning. We present the first integrated work in composing web services end to end from specification to deployment by synergistically combining the strengths of the above approaches. We describe a prototype service creation environment along with a use-case scenario, and demonstrate how it can significantly speed up the time-tomarket for new services.

Data sharing with multiple parties over a third-party distribution framework requires that both data integrity and confidentiality be assured. One of the most widely used data organization structures is the tree structure. When such structures encode sensitive information (such as in XML documents), it is crucial that integrity and confidentiality be assured not only for the content, but also for the structure. Digital signature schemes are commonly used to authenticate the integrity of the data. The most widely used such technique for tree structures is the Merkle hash technique, which however is known to be "not hiding", thus leading to unauthorized leakage of information. Most techniques in the literature are based on the Merkle hash technique and thus suffer from the problem of unauthorized information leakages. Assurance of integrity and confidentiality (no leakages) of tree-structured data is an important problem in the context of secure data publishing and content distribution systems.In this paper, we propose a signature scheme for tree structures, which assures both confidentiality and integrity and is also efficient, especially in third-party distribution environments. Our integrity assurance technique, which we refer to as the "Structural signature scheme", is based on the structure of the tree as defined by tree traversals (pre-order, post-order, in-order) and is defined using a randomized notion of such traversal numbers. In addition to formally defining the technique, we prove that it protects against violations of content and structural integrity and information leakages. We also show through complexity and performance analysis that the structural signature scheme is efficient; with respect to the Merkle hash technique, it incurs comparable cost for signing the trees and incurs lower cost for user-side integrity verification.

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The digital transformation of healthcare ecosystems on the Internet has been rapid and explosive. While web-based and IoT-driven ecosystems promise a future for universally accessible and more intelligent healthcare, the privacy of patients, doctors, nurses, and healthcare providers is of greater concern today than ever. Regulatory requirements are evolving (such as EU's DPR to GDPR) to address such privacy requirements. This special issue addresses two important topics involved in healthcare informatics and privacy. The healthcare and life sciences industries have relied on computing and information technology since the 1950s. 1 In the last decade or so, with the advent of mobile computing, cloud platforms as well as machine learning and analytics, the digital transformation of healthcare on the Internet has been rapid and explosive. The Internet has enabled the transfer and communication of healthcare data as well as the delivery of healthcare services and apps connecting patients and healthcare providers. Digital healthcare ecosystems have evolved on top of cloud platforms, mobile computing, and the Internet of Things (IoT), as well as wearable devices distributed across geopolitical and socioeconomic boundaries. While such ecosystems promise a future for universally accessible and more intelligent healthcare, the privacy of patients, doctors, nurses, and healthcare providers is of greater concern today than ever. Regulatory compliance requirements, such as the US Health Insurance Portability and Accountability Act (HIPAA) and EU General Data Privacy Regulation (GDPR), support the protection of privacy at various levels. Healthcare data breaches not only can cause significant adverse personal and social impacts of patients and their families, but also incur a huge cost: $6.2 Billion USD. 2 Organizations maintain the privacy of the medical status of their C-suite as closely guarded secrets, to protect themselves from adverse reactions from Wall Street and investors. Protection and management of healthcare privacy has several challenges and open problems; for example, how does the application of AI and machine learning for healthcare analytics affect privacy? How can anonymization of genomic and healthcare data be carried out to preserve utility while protecting privacy? How can we protect healthcare devices from leaking sensitive

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