A guide to implementing cloud services

The Australian Government’s policy on cloud computing is that agencies may choose to use cloud computing services where they provide value for money and adequate security, as stated in the April 2011 Australian Government Cloud Computing Strategic Direction Paper1 (the Strategic Direction Paper).   Readers new to cloud computing should read the Strategic Direction Paper which provides an introduction to cloud computing, a definition and an overview of its associated risks and benefits as they apply to Australian Government agencies. The guide supports the Strategic Direction Paper and provides an overarching risk-based approach for agencies to develop an organisational cloud strategy and implement cloud-based services. It is designed as an aid for experienced business strategists, architects, project managers, business analysts and IT staff to realise the benefits of cloud computing technology while managing risks

Energy efficient security and privacy management in sensor clouds

Sensor Cloud is a new model of computing for Wireless Sensor Networks, which facilitates resource sharing and enables large scale sensor networks. A multi-user distributed system, however, where resources are shared, has inherent challenges in security and privacy. The data being generated by the wireless sensors in a sensor cloud need to be protected against adversaries, which may be outsiders as well as insiders. Similarly the code which is disseminated to the sensors by the sensor cloud needs to be protected against inside and outside adversaries. Moreover, since the wireless sensors cannot support complex, energy intensive measures, the security and privacy of the data and the code have to be attained by way of lightweight algorithms. In this work, we first present two data aggregation algorithms, one based on an Elliptic Curve Cryptosystem (ECC) and the other based on symmetric key system, which provide confidentiality and integrity of data against an outside adversary and privacy against an in network adversary. A fine grained access control scheme which works on the securely aggregated data is presented next. This scheme uses Attribute Based Encryption (ABE) to achieve this objective. Finally, to securely and efficiently disseminate code in the sensor cloud, we present a code dissemination algorithm which first reduces the amount of code to be transmitted from the base station. It then uses Symmetric Proxy Re-encryption along with Bloom filters and HMACs to protect the code against eavesdropping and false code injection attacks. --Abstract, page iv

An advanced data fabric architecture leveraging homomorphic encryption and federated learning

Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a centralized repository for machine learning. This paper introduces a secure approach for medical image analysis using federated learning and partially homomorphic encryption within a distributed data fabric architecture. With this method, multiple parties can collaborate in training a machine-learning model without exchanging raw data but using the learned or fused features. The approach complies with laws and regulations such as HIPAA and GDPR, ensuring the privacy and security of the data. The study demonstrates the method's effectiveness through a case study on pituitary tumor classification, achieving a significant level of accuracy. However, the primary focus of the study is on the development and evaluation of federated learning and partially homomorphic encryption as tools for secure medical image analysis. The results highlight the potential of these techniques to be applied to other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning

Personal Data Management Systems: The security and functionality standpoint

International audienceRiding the wave of smart disclosure initiatives and new privacy-protection regulations, the Personal Cloud paradigm is emerging through a myriad of solutions offered to users to let them gather and manage their whole digital life. On the bright side, this opens the way to novel value-added services when crossing multiple sources of data of a given person or crossing the data of multiple people. Yet this paradigm shift towards user empowerment raises fundamental questions with regards to the appropriateness of the functionalities and the data management and protection techniques which are offered by existing solutions to laymen users. These questions must be answered in order to limit the risk of seeing such solutions adopted only by a handful of users and thus leaving the Personal Cloud paradigm to become no more than one of the latest missed attempts to achieve a better regulation of the management of personal data. To this end, we review, compare and analyze personal cloud alternatives in terms of the functionalities they provide and the threat models they target. From this analysis, we derive a general set of functionality and security requirements that any Personal Data Management System (PDMS) should consider. We then identify the challenges of implementing such a PDMS and propose a preliminary design for an extensive and secure PDMS reference architecture satisfying the considered requirements. Finally, we discuss several important research challenges remaining to be addressed to achieve a mature PDMS ecosystem

Blockchain Technology Based Supply Chain Systems and Supply Chain Performance: A Resource-Based View

Blockchain technology (BCT) is set to transform firms’ present ways of managing supply chains. This thesis explores how the efforts by firms to integrate BCT into their supply chain systems and activities, can enable certain supply chain capabilities and subsequently, improve supply chain performance. Using an abductive research approach, qualitative content analyses of 126 cases of firms’ efforts in BCT-based supply chain systems (BCT-SCS) across multiple industries are conducted to identify the BCT-enabled supply chain capabilities and their performance outcomes. Findings reveal that the present BCT-SCS efforts are mainly oriented towards improving the operational-level capabilities namely, information sharing and coordination capabilities rather than strategic-level capabilities namely, integration and collaboration capabilities. The predominant performance outcomes resulting from these capabilities along with the BCT-SCS are quality compliance and improvement, process improvement, flexibility, reduced cost, and reduced process time. However, the performance outcomes vary with industry type, based on the risks that the industry faces. Based on the study’s findings, an integrated framework of research propositions is presented to facilitate future empirical research

Ensuring compliance with data privacy and usage policies in online services

Online services collect and process a variety of sensitive personal data that is subject to complex privacy and usage policies. Complying with the policies is critical, often legally binding for service providers, but it is challenging as applications are prone to many disclosure threats. We present two compliance systems, Qapla and Pacer, that ensure efficient policy compliance in the face of direct and side-channel disclosures, respectively. Qapla prevents direct disclosures in database-backed applications (e.g., personnel management systems), which are subject to complex access control, data linking, and aggregation policies. Conventional methods inline policy checks with application code. Qapla instead specifies policies directly on the database and enforces them in a database adapter, thus separating compliance from the application code. Pacer prevents network side-channel leaks in cloud applications. A tenant’s secrets may leak via its network traffic shape, which can be observed at shared network links (e.g., network cards, switches). Pacer implements a cloaked tunnel abstraction, which hides secret-dependent variation in tenant’s traffic shape, but allows variations based on non-secret information, enabling secure and efficient use of network resources in the cloud. Both systems require modest development efforts, and incur moderate performance overheads, thus demonstrating their usability.Onlinedienste sammeln und verarbeiten eine Vielzahl sensibler persönlicher Daten, die komplexen Datenschutzrichtlinien unterliegen. Die Einhaltung dieser Richtlinien ist häufig rechtlich bindend für Dienstanbieter und gleichzeitig eine Herausforderung, da Fehler in Anwendungsprogrammen zu einer unabsichtlichen Offenlegung führen können. Wir präsentieren zwei Compliance-Systeme, Qapla und Pacer, die Richtlinien effizient einhalten und gegen direkte und indirekte Offenlegungen durch Seitenkanäle schützen. Qapla verhindert direkte Offenlegungen in datenbankgestützten Anwendungen. Herkömmliche Methoden binden Richtlinienprüfungen in Anwendungscode ein. Stattdessen gibt Qapla Richtlinien direkt in der Datenbank an und setzt sie in einem Datenbankadapter durch. Die Konformität ist somit vom Anwendungscode getrennt. Pacer verhindert Netzwerkseitenkanaloffenlegungen in Cloud-Anwendungen. Geheimnisse eines Nutzers können über die Form des Netzwerkverkehr offengelegt werden, die bei gemeinsam genutzten Netzwerkelementen (z. B. Netzwerkkarten, Switches) beobachtet werden kann. Pacer implementiert eine Tunnelabstraktion, die Geheimnisse im Netzwerkverkehr des Nutzers verbirgt, jedoch Variationen basier- end auf nicht geheimen Informationen zulässt und eine sichere und effiziente Nutzung der Netzwerkressourcen in der Cloud ermöglicht. Beide Systeme erfordern geringen Entwicklungsaufwand und verursachen einen moderaten Leistungsaufwand, wodurch ihre Nützlichkeit demonstriert wird