A Secure and Verifiable Computation for k-Nearest Neighbor Queries in Cloud

The popularity of cloud computing has increased significantly in the last few years due to scalability, cost efficiency, resiliency, and quality of service. Organizations are more interested in outsourcing the database and DBMS functionalities to the cloud owing to the tremendous growth of big data and on-demand access requirements. As the data is outsourced to untrusted parties, security has become a key consideration to achieve the confidentiality and integrity of data. Therefore, data owners must transform and encrypt the data before outsourcing. In this paper, we focus on a Secure and Verifiable Computation for k-Nearest Neighbor (SVC-kNN) problem. The existing verifiable computation approaches for the kNN problem delegate the verification task solely to a single semi-trusted party. We show that these approaches are unreliable in terms of security, as the verification server could be either dishonest or compromised. To address these issues, we propose a novel solution to the SVC-kNN problem that utilizes the random-splitting approach in conjunction with the homomorphic properties under a two-cloud model. Specifically, the clouds generate and send verification proofs to end-users, allowing them to verify the computation results efficiently. Our solution is highly efficient from the data owner and query issuers’ perspective as it significantly reduces the encryption cost and pre-processing time. Furthermore, we demonstrated the correctness of our solution using Proof by Induction methodology to prove the Euclidean Distance Verification

Privacy-by-design in big data analytics and social mining

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Authenticated Outlier Mining for Outsourced Databases

The Data-Mining-as-a-Service (DMaS) paradigm is becoming the focus of research, as it allows the data owner (client) who lacks expertise and/or computational resources to outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises some issues about result integrity : how could the client verify the mining results returned by the server are both sound and complete? In this paper, we focus on outlier mining, an important mining task. Previous verification techniques use an authenticated data structure (ADS) for correctness authentication, which may incur much space and communication cost. In this paper, we propose a novel solution that returns a probabilistic result integrity guarantee with much cheaper verification cost. The key idea is to insert a set of artificial records ( $AR$ A R s) into the dataset, from which it constructs a set of artificial outliers ( $AO$ A O s) and artificial non-outliers ( $ANO$ A N O s). The $AO$ A O s and $ANO$ A N O s are used by the client to detect any incomplete and/or incorrect mining results with a probabilistic guarantee. The main challenge that we address is how to construct $AR$ A R s so that they do not change the (non-)outlierness of original records, while guaranteeing that the client can identify $ANO$ A N O s and $AO$ A O s without executing mining. Furthermore, we build a strategic game and show that a Nash equilibrium exists only when the server returns correct outliers. Our implementation and experiments demonstrate that our verification solution is efficient and lightweight

Catch, Clean, and Release: A Survey of Obstacles and Opportunities for Network Trace Sanitization

Network researchers benefit tremendously from access to traces of production networks, and several repositories of such network traces exist. By their very nature, these traces capture sensitive business and personal activity. Furthermore, network traces contain significant operational information about the target network, such as its structure, identity of the network provider, or addresses of important servers. To protect private or proprietary information, researchers must “sanitize” a trace before sharing it. \par In this chapter, we survey the growing body of research that addresses the risks, methods, and evaluation of network trace sanitization. Research on the risks of network trace sanitization attempts to extract information from published network traces, while research on sanitization methods investigates approaches that may protect against such attacks. Although researchers have recently proposed both quantitative and qualitative methods to evaluate the effectiveness of sanitization methods, such work has several shortcomings, some of which we highlight in a discussion of open problems. Sanitizing a network trace, however challenging, remains an important method for advancing network–based research

Technological, organisational, and environmental factors affecting the adoption of blockchain-based distributed identity management in organisations

Background: Blockchain is a disruptive technology with the potential to innovate businesses. Ignoring or resisting it might result in a competitive disadvantage for organisations. Apart from its original financial application of cryptocurrency, other applications are emerging, the most common being supply chain management and e-voting systems. However, there is less focus on information and cybersecurity applications, especially from the enterprise perspective. This research addresses this knowledge gap, focussing on its application of distributed identity management in organisations. Objectives: The main objective is to investigate technological, organisational, and environmental (TOE) factors affecting the adoption of blockchain-based distributed identity management (BDIDM) in organisations to determine the most critical factors. Secondary objectives include determining whether the blockchain type affects BDIDM adoption and whether the TOE-BDIDM model measuring the phenomenon is effective and appropriate. But given the relative newness of blockchain, the initial goal consists of intensively exploring the topic to understand the practicality of adopting BDIDM in organisations and establishing whether claims made around it are factual than just due to the blockchain hype. Methodology: The study uses meta-synthesis to explore the topic, summarising 69 papers selected qualitatively from reputed academic sources. The study then surveys 111 information and cybersecurity practitioners selected randomly in South African organisations to investigate the TOE factors affecting BDIDM adoption. To do so, it utilises an online questionnaire rooted in an adapted TOE model called TOE-BDIDM as a data collection instrument. The analysis of this primary data is purely quantitative and includes (i) Structural Equation Modelling (SEM) of the measurement model, i.e. confirmatory factor analysis (CFA); (ii) binary logistics regression analysis; and (iii) Chi-Square tests Results: Meta-synthesis revealed theoretical grounds underlying claims made around the topic while spotting diverging views about BDIDM practicality for the enterprise context. It also identifies the TOE theory as more suitable to explain the phenomenon. Binary logistics regression modelling reveals that TOE factors do affect BDIDM adoption in organisations, either positively or negatively. The factors predict BDIDM adopters and non-adopters, with Technology Characteristics being the most critical factor and the most that could predict BDIDM non-adopters. Organisation Readiness was the second critical factor, the most that could predict BDIDM adopters. Overall, TOE-BDIDM effectively predicted 92.5% of adopters and 45.2% of non-adopters. CFA indicates that TOE-BDIDM appropriateness for investigating the phenomenon is relatively fair. The Chi-Square tests reveal a significant association between Blockchain Type and BDIDM adoption. Implications: The discussion highlights various implications of the above findings, including the plausibility of the impartiality of typical privacy-preserving BDIDM models like the Selfsovereign identity: The majority of respondents preferred private permissioned blockchain, which tends to be centralised, more intermediated, and less privacy-preserving. The rest implications relate to the disruptiveness nature of BDIDM and the BDIDM adoption being more driven by technological than organisational or environmental factors. The study ends by reflecting on the research process and providing fundamental limitations and recommendations for future researc

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse