Sensitive Business Information Research Articles

DIGITAL TRANSFORMATION AND DATA PROTECTION

In today’s interconnected world, t he incorporation of digital technology across all areas of an organization is the key focus of digital transformation, a strategic business initiative. It involves the assessment and modernization of an organization's processes, products, operations, and technology stack to facilitate ongoing, rapid, customer-driven innovation. Digital transformation goes beyond traditional functions such as sales, marketing, and customer service, centering instead on rethinking and engaging with customers. Moving from paper to spreadsheets to intelligent applications for business management presents an opportunity to rethink how we conduct business and interact with our customers, leveraging digital technology in the process. similarly cybersecurity and data privacy have become critical concerns for businesses of all sizes. This paper explores the role of Digital transformation and the evolving landscapes of cyber threats and the importance of robust cybersecurity measures in safeguarding sensitive business information. It examines the various types of cyberattacks, including malware, phishing, and ransomware, that can compromise business operations and erode consumer trust. Additionally, the paper highlights the significance of data privacy in maintaining compliance with regulations such as GDPR and CCPA, and in protecting the personal information of customers and employees.

How do investors value the publication of tax information? Evidence from the European public country‐by‐country reporting

AbstractWe examine the costs associated with public disclosure, as opposed to confidential reporting, of tax country‐by‐country reporting (CbCR) information. Our study addresses a critical knowledge gap, considering the growing adoption of public tax transparency measures. We aim to illuminate this matter by examining the expected costs for firms of making previously confidential CbCR information publicly available. The fact that the information was previously confidentially reported to the tax authorities allows us to assess the cost of publication in isolation. Employing an event study methodology, we provide early evidence on the capital market reaction to this new requirement on a sample of European firms falling within its scope. We document a significantly negative cumulative average abnormal return of EUR 47 billion to 64 billion for up to 3 days following the announcement. Additional cross‐sectional results suggest that concerns about the reputational costs arising from public scrutiny and the proprietary costs from disclosing sensitive business information outweigh the potential benefits of an extended information environment from an investor perspective. Our findings highlight that the public disclosure of tax information imposes significant—and likely unintended—costs from a firm perspective. This aspect should be carefully considered when developing tax transparency measures.

BLOCKCHAIN TECHNOLOGY AND APPLICATION – SMEs ASPECT

<em>This review article is designed for SME owners and directors to understand better what blockchains are and how they work and to support them to start an entirely new blockchain or fork an existing one and create a new token. The article presents technology bases and how blockchains can serve SMEs and foreseeable challenges that could jeopardize the achieved results. Particular attention the authors paid to blockchain applications, transaction cost reduction, and sensitive business information security. <em>Even more attention the authors gave to the challenges</em> connected with digital infrastructures and business practices, investments, lack of interoperability, lack of skills, and legal aspects. <em>Chapter four discusses the creation of blockchains for SMEs </em>from the aspects of choosing a type of blockchain, choosing of consensus mechanism, a platform selection, nodes design, operating systems, instances design, application programming interfaces, and user interfaces, and the blockchain finalizing. In chapter five, Conclusions, the authors draw conclusions based on the work presented in the article.</em>

Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT

Internet of Things (IoT) devices are becoming increasingly ubiquitous in daily life. They are utilized in various sectors like healthcare, manufacturing, and transportation. The main challenges related to IoT devices are the potential for faults to occur and their reliability. In classical IoT fault detection, the client device must upload raw information to the central server for the training model, which can reveal sensitive business information. Blockchain (BC) technology and a fault detection algorithm are applied to overcome these challenges. Generally, the fusion of BC technology and fault detection algorithms can give a secure and more reliable IoT ecosystem. Therefore, this study develops a new Blockchain Assisted Data Edge Verification with Consensus Algorithm for Machine Learning (BDEV-CAML) technique for IoT Fault Detection purposes. The presented BDEV-CAML technique integrates the benefits of blockchain, IoT, and ML models to enhance the IoT network’s trustworthiness, efficacy, and security. In BC technology, IoT devices that possess a significant level of decentralized decision-making capability can attain a consensus on the efficiency of intrablock transactions. For fault detection in the IoT network, the deep directional gated recurrent unit (DBiGRU) model is used. Finally, the African vulture optimization algorithm (AVOA) technique is utilized for the optimal hyperparameter tuning of the DBiGRU model, which helps in improving the fault detection rate. A detailed set of experiments were carried out to highlight the enhanced performance of the BDEV-CAML algorithm. The comprehensive experimental results stated the improved performance of the BDEV-CAML technique over other existing models with maximum accuracy of 99.6%.

Performance Evaluation of Cryptographic Security Algorithms on Cloud

Cloud computing is a cost-effective approach to provide on demand computing and data storage solutions. Data storage is one of the key services provided by the cloud. Cloud offers improved efficiency, flexibility, and scalability, but all these advantages can be overturned if security is not taken into consideration. It is the cloud vender’s responsibility to keep the data safe with highly secure cloud services, which helps them earn the trust of the customers. Today, cloud security is critical since most organizations are already using cloud computing in alternate forms. There is always a concern that the highly sensitive business information and intellectual property may be exposed or misused due to increasingly sophisticated cyber threats. This research paper provides a distributed architecture for cloud data security which is independent of the underlying platforms. A cloud security architecture provides written and visual model to define how to configure and secure operations within the cloud. This paper compares the performance of RC4 against AES-256. The performance of the proposed encryption algorithms is evaluated on a widely used database MySQL. This paper provides a better solution to ensure the security of cloud databases by using two encryption algorithms.

AppClassNet

The recent success of Artificial Intelligence (AI) is rooted into several concomitant factors, namely theoretical progress coupled with abundance of data and computing power. Large companies can take advantage of a deluge of data, typically withhold from the research community due to privacy or business sensitivity concerns, and this is particularly true for networking data. Therefore, the lack of high quality data is often recognized as one of the main factors currently limiting networking research from fully leveraging AI methodologies potential. Following numerous requests we received from the scientific community, we release AppClassNet, a commercial-grade dataset for benchmarking traffic classification and management methodologies. AppClassNet is significantly larger than the datasets generally available to the academic community in terms of both the number of samples and classes, and reaches scales similar to the popular ImageNet dataset commonly used in computer vision literature. To avoid leaking user- and business-sensitive information, we opportunely anonymized the dataset, while empirically showing that it still represents a relevant benchmark for algorithmic research. In this paper, we describe the public dataset and our anonymization process. We hope that AppClassNet can be instrumental for other researchers to address more complex commercial-grade problems in the broad field of traffic classification and management.

Forensic Investigation of Remnant Data on USB Storage Devices Sold in New Zealand

The digital forensic tools used by law enforcement agencies for forensic investigations are mostly proprietary and commercially expensive; although open-source tools are used, the investigations conducted with such tools are not verified by reputable organisations, and hence, users are reluctant to practice such tools. To address this issue, we experimentally evaluate three open-source forensic tools based on various requirements recommended by the National Institute of Standards and Technology (NIST) framework for forensic investigation. The experimental setup consists of a forensic workstation, write-blocker, and purchased USB hard drives investigated via digital forensic imaging tools, i.e., DC3DD, DCFLDD, and Guymager. We create various test cases, which distribute USB hard drives in different groups and investigate the functional and optional requirements of NIST along with recovering and analysing remnant data. We evaluate these forensic tools by analysing the log information, following, anonymously (to ensure that data were not disclosed or misused during or after the investigations) collecting, examining, and classifying the remnant data restored from the USB hard drives. We observe that the percentage of hardware resources usage and the processing time of each tool are remarkably different, e.g., Guymager was the fastest tool and met all the functional requirements in each test case, but it utilised more CPU and memory resources than DC3DD, DCFLDD. We note that 88.23% of the USB hard drives contained sensitive personal or business information (e.g., personal photos, bank transactions, and contracts). Subsequently, the remnant data analysis shows that consumers in New Zealand are unaware of personal data security and the associated vulnerabilities of data leakages.

Performance degradation in parallel-server systems with shared resources and lack of coordination

Parallel server systems are ubiquitous. Multicore CPUs are in practically every personal device from mobile handsets to high-end desktop PCs. At larger scale, data centers consist of a huge number of physical servers often shared by multiple users (for economic reasons). Moreover, the simultaneous users are typically unaware of each other due to reasons that can be technical (cf. security & privacy), practical (coordination layer would add complexity) and business related (usage can be business sensitive information). The workload patterns of different users may also vary significantly. This results in unpredictable and often long response times. We study means for tackling these challenges. In particular, we consider a model where multiple users (dispatchers) route their jobs to a pool of servers using uncoordinated static dispatching policies. The goal is to determine how different policies interact: whether users’ decisions support each other, or if some decisions are simply counterproductive. The lack of proper coordination is shown to increase the mean response times, with two common and robust dispatching policies: Size-Interval-Task Assignment (SITA) and Round-Robin (RR). We refer to this phenomenon as the price of ignorance.

Service charge management at the dawning of the age of the professional statement

PurposeThe paper provides a snapshot analysis on the state of service charge management at the point in which its regulatory framework by RICS changed from a voluntary code of practice to a mandatory professional statement.Design/methodology/approachThe data consist of a unique eight-year longitudinal study of service charge statements and practice (2010–2017). Because of the confidential nature of such business-sensitive information, this is a priceless study of real-world practice over such a long period and is able to illustrate both annual compliance and the year-on-year changes. Given this, it is recognised that data are skewed in favour of compliance because they are derived from an actively managed portfolio.FindingsThe results continue to illustrate long-running problems of non-compliance with “required” metrics. Given the inherent bias in the data, this is especially difficult to excuse. The paper also analyses the results in the light of the new RICS professional statement, which requires mandatory compliance. Whilst some of the metrics are advisory, there remain questions over how RICS might realistically enforce so many practitioners to change their existing performance and how willing the institution might be to actually prosecute failure. It also revisits the issue of institutionalised benchmarking of standards. Intriguingly, there are islands of almost perfect compliance, which offers an interesting contrast and raises further research questions on why some practitioners provide such exemplary work.Research limitations/implicationsThe data are derived from the clients of a UK property management consultancy. This does preclude any randomness to the sampling. However, the richness of the data and the methodology adopted provide valid data.Originality/valueThis work offers both unique data and an eight-year longitudinal analysis, but also a timely comparison with the requirements within a new RICS professional statement. This shift in regulatory regime reinforces the value of the work.

VIDPSO: Victim item deletion based PSO inspired sensitive pattern hiding algorithm for dense datasets

Collaborative frequent itemset mining involves analyzing the data shared from multiple business entities to find interesting patterns from it. However, this comes at the cost of high privacy risk. Because some of these patterns may contain business-sensitive information and hence are denoted as sensitive patterns. The revelation of such patterns can disclose confidential information. Privacy-preserving data mining (PPDM) includes various sensitive pattern hiding (SPH) techniques, which ensures that sensitive patterns do not get revealed when data mining models are applied on shared datasets. In the process of hiding sensitive patterns, some of the non-sensitive patterns also become infrequent. SPH techniques thus affect the results of data mining models. Maintaining a balance between data privacy and data utility is an NP-hard problem because it requires the selection of sensitive items for deletion and also the selection of transactions containing these items such that side effects of deletion are minimal. There are various algorithms proposed by researchers that use evolutionary approaches such as genetic algorithm(GA), particle swarm optimization (PSO) and ant colony optimization (ACO). These evolutionary SPH algorithms mask sensitive patterns through the deletion of sensitive transactions. Failure in the sensitive patterns masking and loss of data have been the biggest challenges for such algorithms. The performance of evolutionary algorithms further gets degraded when applied on dense datasets. In this research paper, victim item deletion based PSO inspired evolutionary algorithm named VIDPSO is proposed to sanitize the dense datasets. In the proposed algorithm, each particle of the population consists of n number of sub-particles derived from pre-calculated victim items. The proposed algorithm has a high exploration capability to search the solution space for selecting optimal transactions. Experiments conducted on real and synthetic dense datasets depict that VIDPSO algorithm performs better vis-a-vis GA, PSO and ACO based SPH algorithms in terms of hiding failure with minimal loss of data.

Robust and Privacy-Preserving Service Recommendation over Sparse Data in Education

Service recommendation has become one of the most effective approaches to quickly extract insightful information from big educational data. However, the sparsity of educational service quality data (from multiple platforms or parties) used to make service recommendations often leads to few even null recommended results. Moreover, to protect sensitive business information and obey laws, preserving user privacy during the abovementioned multisource data integration process is a very important but challenging requirement. Considering the above challenges, this paper integrates Locality-Sensitive Hashing (LSH) with hybrid Collaborative Filtering (HCF) techniques for robust and privacy-aware data sharing between different platforms involved in the cross-platform service recommendation process. Furthermore, to minimize the “False negative” recommended results incurred by LSH and enhance the success of recommended results, we propose two optimization strategies to reduce the probability that similar neighbours of a target user or similar services of a target service are overlooked by mistake. Finally, we conduct a set of experiments based on a real distributed service quality dataset, i.e., WS-DREAM, to validate the feasibility and advantages of our proposed recommendation approach. The extensive experimental results show that our proposal performs better than three competitive methods in terms of efficiency, accuracy, and successful rate while guaranteeing privacy-preservation.

Absence of personal relationship in a buyer-supplier relationship: case of buyers and suppliers of logistics services provider in Australia

While the importance of personal relationships (e.g. friendship) within supply chain relationships is well established, a gap remains on negative aspects associated with an absence of a personal relationship in the buyer-supplier relationship in the supply chain context. Because there is very limited research in this area, a case study method is employed via semi-structured interviews with 24 senior managers from 10 different companies based in Australia engaged in the process of buying and selling logistics services. Results reveal that an absence of a personal relationship between managers of buying and supplying firms in the supply chain leads to reduced trust, non-sharing of important business ideas and sensitive business information, no peer mentoring, delay in resolving conflicts and low employee engagement. The paper concludes by discussing the contribution to practice, research limitations, and future research directions.

Former GSK scientist admits to IP theft

Yu Xue, a Ph.D. biochemist who worked at GlaxoSmithKline’s research facility in Upper Merion, Pa., has pleaded guilty to stealing intellectual property (IP) from her former employer, according to the U.S. Department of Justice. She has not yet been sentenced. Xue planned to use the pilfered research to benefit a Chinese drug company, Renopharma, that she and her associates had set up with financial support from the Chinese government, the Justice Department says. According to the original 2016 indictment, Xue was accused of stealing IP on pharmaceuticals under development, including a monoclonal antibody similar to the cancer therapy Herceptin. Over time, the partners aimed to develop their own antibody drugs. “We cannot allow U.S. citizens or foreign nationals to steal sensitive business information and hand it over to competitors in other countries,” says William M. McSwain, the U.S. attorney for the Eastern District of Pennsylvania in Philadelphia. Trade-secret theft has

An analysis of utility meter data aggregation and tenant privacy to support energy use disclosure in commercial buildings

A growing number of cities are adopting energy use benchmarking ordinances, which require building owners report their buildings' total energy usage annually. It also requires that utilities supply aggregated building-level monthly energy consumption data. The data aggregation poses privacy concerns, as it is possible to estimate the individual tenant load consumption curve by dividing aggregated energy data by the number of meters. A solution is to quantify and assess the impact of adjusting the utility meter aggregation threshold on tenant privacy and on buildings that are eligible for energy usage reporting. As the threshold increases, fewer buildings are eligible for energy use data disclosure and therefore lessening data value. This study aims to investigate the similarity between individual utility meters and whole-building totals at various aggregation levels. Based on statistical analysis of 715,000 anonymized, non-residential meter accounts from six utilities across the U.S., we developed the “Meter Aggregation Selection Threshold” as a metric to assess tenant privacy risk. The metric estimates the portion of individual customer energy use patterns that are similar to the aggregated building consumption profile. It allows policy makers to make an informed decision on whether required disclosure regulations compromise business sensitive information and tenant privacy.

Analyzing critical material demand: A revised approach

Apparent consumption has been widely used as a metric to estimate material demand. However, with technology advancement and complexity of material use, this metric has become less useful in tracking material flows, estimating recycling feedstocks, and conducting life cycle assessment of critical materials. We call for future research efforts to focus on building a multi-tiered consumption database for the global trade network of critical materials. This approach will help track how raw materials are processed into major components (e.g., motor assemblies) and eventually incorporated into complete pieces of equipment (e.g., wind turbines). Foreseeable challenges would involve: 1) difficulty in obtaining a comprehensive picture of trade partners due to business sensitive information, 2) complexity of materials going into components of a machine, and 3) difficulty maintaining such a database. We propose ways to address these challenges such as making use of digital design, learning from the experience of building similar databases, and developing a strategy for financial sustainability. We recommend that, with the advancement of information technology, small steps toward building such a database will contribute significantly to our understanding of material flows in society and the associated human impacts on the environment.

An exact decomposition method to save trips in cooperative pickup and delivery based on scheduled trips and profit distribution

Compared to the non-cooperative mode, the cooperative mode is a powerful way to reduce operational cost in pickup and delivery service. In order to protect business sensitive information, sometimes participants are unwilling to open the customer's detailed information. Thus, we utilize the publishable trip scheduled results to compute the saved trips brought by cooperation. A mathematical model minimizing trips of cooperation is proposed. To obtain the exact solution, we define the cooperative trip set. We prove that only when cooperative trip set exists it is possible to save trips by cooperation. For a two-trip cooperative trip set, we exactly obtain the saved trips by enumerating all feasible cooperative cases. For a K-trip cooperative trip set, we propose an exact method to obtain the saved trips by decomposing it to at most K-1 two-trip cooperative trip sets. Computational complexity of the based-on-decomposition exact algorithm is O(N), where N is the total number of trips. Using the based-on-decomposition algorithm, we calculate the exact Shapley value to distribute profit. To empirically verify the exact method, we perform the extensive experiment cases of the real cooperative pickup and delivery service, i.e., “picking up and delivering customers to airport service” (PDCA).

Integrating novel data streams to support biosurveillance in commercial livestock production systems in developed countries: challenges and opportunities.

Reducing the burden of emerging and endemic infectious diseases on commercial livestock production systems will require the development of innovative technology platforms that enable information from diverse animal health resources to be collected, analyzed, and communicated in near real-time. In this paper, we review recent initiatives to leverage data routinely observed by farmers, production managers, veterinary practitioners, diagnostic laboratories, regulatory officials, and slaughterhouse inspectors for disease surveillance purposes. The most commonly identified challenges were (1) the lack of standardized systems for recording essential data elements within and between surveillance data streams, (2) the additional time required to collect data elements that are not routinely recorded by participants, (3) the concern over the sharing and use of business sensitive information with regulatory authorities and other data analysts, (4) the difficulty in developing sustainable incentives to maintain long-term program participation, and (5) the limitations in current methods for analyzing and reporting animal health information in a manner that facilitates actionable response. With the significant recent advances in information science, there are many opportunities to develop more sophisticated systems that meet national disease surveillance objectives, while still providing participants with valuable tools and feedback to manage routine animal health concerns.

Importance of Cyber Security

military, organizations, financial institutions, universities and other businesses collect, process and store a large amount of confidential information and data on computers and transmit that data over networks to other computers. With the continuous rapid growth of volume and sophistication of cyber attacks, quick attempts are required to secure sensitive business and personal information, as well as to protect national security. The paper details about the nature of cyberspace and shows how the internet is unsecure to transmit the confidential and financial information. We demonstrate that hacking is now common and harmful for global economy and security and presented the various methods of cyber attacks in India and worldwide. Keywordssecurity, Cyberspace, Cybercrime.

‘Administrative Protective Orders: Protection of Confidential Information in Canadian Trade Remedy Proceedings’

Trade remedy disputes require parties to adduce evidence that includes commercially sensitive business information and strategies. Canada's trade remedy system seeks to strike a balance between the interests of parties in preventing harmful disclosure of confidential information and the public's need for transparent and expedient administration of justice. This paper provides an overview of the mechanisms and procedures available in Canada for the treatment and disclosure of confidential information in the context of trade remedy disputes and judicial review of those decisions, and includes a discussion of existing challenges and areas of concern in the Canadian context.

SecTTS: A secure track & trace system for RFID-enabled supply chains

Due to the highly sensitive business information communicated within RFID-enabled supply chain networks, there is an urgent need for a secure and effective track & trace system. In this paper, we aim to design and implement a secure track & trace system (SecTTS) based on the EPCglobal network where the EPC Discovery Services (EPCDSs) is the most important component. In SecTTS, the EPCDS is designed as a semi-trusted server, contrasting the assumptions of previous works, which requires a trusted EPCDS. Our EPCDS is implemented based on the relay model where set-based relay policy is proposed to allow each supply chain partner to define different relay policies for information with different sensitivity. We implement a prototype of SecTTS and conduct extensive experiments. The results validate that our design is practical and cost effective.