Privacy is as a critical issue in the age of data. Organizations and corporations who publicly share their data always have a major concern that their sensitive information may be leaked or extracted by rivals or attackers using data miners. High-utility itemset mining (HUIM) is an extension to frequent itemset mining (FIM) which deals with business data in the form of transaction databases, data that is also in danger of being stolen. To deal with this, a number of privacy-preserving data mining (PPDM) techniques have been introduced. An important topic in PPDM in the recent years is privacy-preserving utility mining (PPUM). The goal of PPUM is to protect the sensitive information, such as sensitive high-utility itemsets, in transaction databases, and make them undiscoverable for data mining techniques. However, available PPUM methods do not consider the generalization of items in databases (categories, classes, groups, etc.). These algorithms only consider the items at a specialized level, leaving the item combinations at a higher level vulnerable to attacks. The insights gained from higher abstraction levels are somewhat more valuable than those from lower levels since they contain the outlines of the data. To address this issue, this work suggests two PPUM algorithms, namely MLHProtector and FMLHProtector, to operate at all abstraction levels in a transaction database to protect them from data mining algorithms. Empirical experiments showed that both algorithms successfully protect the itemsets from being compromised by attackers.

HTAP Database as a Service Market Outlook

According to our latest research, the Global HTAP Database as a Service market size was valued at $2.4 billion in 2024 and is projected to reach $13.7 billion by 2033, expanding at a robust CAGR of 21.8% during the forecast period of 2025–2033. The primary factor fueling this remarkable growth is the increasing demand for real-time data processing and analytics across diverse industry verticals. As enterprises strive to gain actionable insights from both transactional and analytical data simultaneously, the adoption of HTAP (Hybrid Transactional/Analytical Processing) Database as a Service solutions is accelerating at an unprecedented pace. This surge is further propelled by the rapid proliferation of cloud computing, digital transformation initiatives, and the need for scalable, agile data infrastructure to support next-generation business applications.

Regional Outlook

North America currently dominates the global HTAP Database as a Service market, accounting for over 38% of the total market share in 2024. This region’s leadership can be attributed to its mature technology ecosystem, early adoption of advanced database solutions, and the presence of major cloud service providers and technology innovators. The United States, in particular, has been at the forefront, with organizations in sectors such as BFSI, healthcare, and retail rapidly integrating HTAP solutions to enable real-time decision-making and personalized customer experiences. Supportive regulatory frameworks, substantial IT budgets, and a highly skilled workforce further consolidate North America’s preeminence in the HTAP Database as a Service market. The region is expected to maintain its leading position throughout the forecast period, albeit with intensifying competition from emerging markets.

The Asia Pacific region is forecasted to be the fastest-growing market for HTAP Database as a Service, with a projected CAGR of 26.4% from 2025 to 2033. This exceptional growth is underpinned by the rapid digitalization of enterprises, burgeoning investments in cloud infrastructure, and increasing awareness of the business value of real-time analytics. Countries such as China, India, Japan, and South Korea are witnessing a surge in demand for agile database solutions, driven by expanding e-commerce, fintech innovation, and government-led digital initiatives. The influx of venture capital, the rise of local cloud providers, and partnerships between global technology leaders and regional firms are further accelerating market expansion. As organizations in Asia Pacific seek to modernize their IT landscapes, the adoption of HTAP Database as a Service is poised for exponential growth.

Emerging economies in Latin America, the Middle East, and Africa are also displaying significant potential, though growth is tempered by unique adoption challenges. While these regions are experiencing increased interest in HTAP solutions due to the expansion of digital services and mobile banking, factors such as limited IT infrastructure, skills shortages, and regulatory uncertainties can impede rapid uptake. Nevertheless, localized demand is rising as governments and enterprises recognize the strategic importance of real-time data processing for economic competitiveness. Targeted policy reforms, public-private partnerships, and investments in digital literacy are gradually overcoming barriers, laying the groundwork for sustained, albeit gradual, market expansion in these emerging regions.

Report Scope

Distributed SQL Database Market Outlook

According to our latest research, the Global Distributed SQL Database market size was valued at $1.2 billion in 2024 and is projected to reach $7.8 billion by 2033, expanding at a robust CAGR of 23.1% during the forecast period of 2025–2033. The primary driver fueling this remarkable growth is the escalating demand for highly available, horizontally scalable, and resilient database architectures among enterprises undergoing digital transformation. As organizations increasingly migrate mission-critical workloads to the cloud and require real-time, global data consistency, distributed SQL databases have emerged as a pivotal solution, offering both the scalability of NoSQL systems and the transactional guarantees of traditional relational databases. This convergence of scalability and consistency is proving indispensable in supporting modern application workloads, especially in industries where uptime, performance, and data integrity are non-negotiable.

Regional Outlook

North America currently commands the largest share of the Distributed SQL Database market, accounting for approximately 38% of the global revenue in 2024. This dominance is underpinned by a mature IT ecosystem, widespread adoption of cloud-native architectures, and a high concentration of technology-forward enterprises across sectors such as BFSI, IT and telecommunications, and retail. The United States, in particular, is home to major distributed SQL database vendors and benefits from a vibrant culture of innovation, robust venture capital activity, and proactive regulatory frameworks that encourage digital infrastructure modernization. Furthermore, North American enterprises are early adopters of hybrid and multi-cloud strategies, which necessitate distributed databases capable of maintaining strong consistency and low latency across diverse environments.

Asia Pacific is poised to be the fastest-growing region in the Distributed SQL Database market with an anticipated CAGR of 27.5% from 2025 to 2033. This rapid growth is driven by surging investments in digital transformation initiatives, especially in China, India, Japan, and Southeast Asia. Enterprises in these economies are actively modernizing their IT infrastructures, with a particular focus on cloud migration, real-time analytics, and omnichannel customer experiences. Government-led smart city projects, expanding fintech ecosystems, and the proliferation of e-commerce platforms are further spurring demand for distributed SQL databases that can handle massive transaction volumes and deliver high availability across geographically dispersed locations. As a result, global and regional vendors are intensifying their presence and partnerships in Asia Pacific to capitalize on this burgeoning opportunity.

Emerging markets in Latin America, the Middle East, and Africa are also witnessing a gradual uptick in distributed SQL database adoption, albeit from a lower base. These regions face unique challenges such as limited IT infrastructure, budget constraints, and a shortage of skilled database professionals. However, localized demand is being catalyzed by the rise of digital banking, regulatory mandates for data sovereignty, and the increasing digitization of public services. Policy reforms aimed at fostering technology adoption and the entry of global cloud service providers are beginning to bridge the digital divide, but market penetration remains uneven. Overcoming barriers such as connectivity issues and legacy system integration will be crucial for unlocking the full potential of distributed SQL databases in these emerging economies.

Report Scope

Distributed SQL Database as a Service Market Outlook

According to our latest research, the global Distributed SQL Database as a Service market size reached USD 1.12 billion in 2024, reflecting robust momentum in cloud-native database adoption. The market is poised for substantial growth, projected to expand at a CAGR of 25.6% from 2025 to 2033. By the end of 2033, the market is expected to achieve a value of approximately USD 8.8 billion. This remarkable growth trajectory is primarily driven by enterprises’ increasing demand for high-availability, scalable, and globally distributed data management solutions, as well as the proliferation of cloud infrastructure and digital transformation initiatives across all major industries.

A key growth factor for the Distributed SQL Database as a Service market is the rapid shift towards cloud-native architectures and microservices-based applications. Enterprises are increasingly realizing the limitations of traditional relational databases in handling globally distributed workloads and mission-critical, real-time transactional data. The need for elastic scalability, continuous availability, and seamless geo-distribution has propelled organizations to adopt distributed SQL databases delivered as a service. This shift is further reinforced by the growing adoption of hybrid and multi-cloud strategies, which require databases capable of operating efficiently across diverse cloud and on-premises environments. As organizations prioritize agility and business continuity, the demand for Distributed SQL Database as a Service is expected to accelerate over the forecast period.

Another significant driver is the surge in data volumes generated by digital business processes, IoT devices, and customer-facing applications. Modern enterprises, especially those in sectors such as BFSI, retail, e-commerce, and telecommunications, require robust data platforms that can process, analyze, and store massive amounts of structured and semi-structured data in real time. Distributed SQL Database as a Service solutions offer horizontal scaling, strong consistency, and automated failover, making them ideal for supporting high-throughput transaction management and analytics workloads. Furthermore, the integration of advanced security features, compliance capabilities, and automated management tools has made these solutions attractive for organizations seeking to reduce operational complexity and total cost of ownership.

The market’s expansion is also fueled by the increasing focus on digital transformation and modernization of legacy IT systems. As enterprises embark on cloud migration journeys, they are leveraging Distributed SQL Database as a Service to modernize their data infrastructure, enhance application performance, and improve customer experiences. The proliferation of SaaS, mobile, and edge computing applications necessitates databases that can operate seamlessly across geographies and deliver low-latency access to data. Additionally, the availability of flexible deployment models, including public, private, and hybrid clouds, allows organizations to tailor their database strategies to meet regulatory, security, and performance requirements. These factors collectively contribute to the sustained growth of the Distributed SQL Database as a Service market.

From a regional perspective, North America continues to dominate the Distributed SQL Database as a Service market, accounting for the largest revenue share in 2024, owing to the early adoption of cloud technologies and the presence of leading technology vendors. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, increased cloud investments, and expanding IT infrastructure in countries such as China, India, and Japan. Europe also demonstrates strong growth potential, supported by stringent data protection regulations and the rising adoption of cloud-based database solutions among enterprises. Latin America and the Middle East & Africa are gradually catching up, with increasing awareness and investments in cloud-native data platforms. The regional landscape is expected to evolve further as organizations worldwide embrace distributed database technologies to gain competitive advantage.

Privacy is as a critical issue in the age of data. Organizations and corporations who publicly share their data always have a major concern that their sensitive information may be leaked or extracted by rivals or attackers using data miners. High-utility itemset mining (HUIM) is an extension to frequent itemset mining (FIM) which deals with business data in the form of transaction databases, data that is also in danger of being stolen. To deal with this, a number of privacy-preserving data mining (PPDM) techniques have been introduced. An important topic in PPDM in the recent years is privacy-preserving utility mining (PPUM). The goal of PPUM is to protect the sensitive information, such as sensitive high-utility itemsets, in transaction databases, and make them undiscoverable for data mining techniques. However, available PPUM methods do not consider the generalization of items in databases (categories, classes, groups, etc.). These algorithms only consider the items at a specialized level, leaving the item combinations at a higher level vulnerable to attacks. The insights gained from higher abstraction levels are somewhat more valuable than those from lower levels since they contain the outlines of the data. To address this issue, this work suggests two PPUM algorithms, namely MLHProtector and FMLHProtector, to operate at all abstraction levels in a transaction database to protect them from data mining algorithms. Empirical experiments showed that both algorithms successfully protect the itemsets from being compromised by attackers.

Time Series Database for Financial Services Market Outlook

As per our latest research, the global Time Series Database for Financial Services market size in 2024 reached USD 1.85 billion, demonstrating robust growth driven by the increasing adoption of real-time analytics and data-driven decision-making in the financial sector. The market is expected to expand at a CAGR of 13.2% from 2025 to 2033, reaching a forecasted value of USD 5.44 billion by 2033. The primary growth factor for this market is the escalating volume of financial transactions and the growing need for high-frequency data analysis, which is crucial for risk management, fraud detection, and algorithmic trading across global financial institutions.

One of the most significant growth drivers for the Time Series Database for Financial Services market is the exponential rise in digital transactions and the proliferation of fintech solutions. Financial institutions are increasingly leveraging time series databases to process and analyze vast streams of transactional data in real time. This capability is essential for supporting complex applications such as algorithmic trading, which relies on millisecond-level data precision to execute trades and manage portfolios efficiently. The surge in mobile banking, online payments, and digital wallets has further amplified the demand for scalable and high-performance databases that can handle the velocity, volume, and variety of financial data generated every second. As financial services become more digitized, the need for robust data infrastructure continues to intensify, propelling the market forward.

Another critical factor fueling market growth is the regulatory environment and the increasing emphasis on compliance and risk management. Financial institutions are under mounting pressure to comply with stringent regulations imposed by global authorities, which necessitate comprehensive data tracking, auditing, and reporting capabilities. Time series databases offer an efficient way to store and retrieve historical data, making it easier for banks, investment firms, and insurance companies to demonstrate compliance and quickly respond to regulatory inquiries. Moreover, the integration of advanced analytics and artificial intelligence with time series databases enables organizations to detect anomalies, predict risks, and automate compliance workflows, thereby reducing operational costs and mitigating potential penalties.

Technological advancements and the rise of cloud computing are also pivotal in shaping the growth trajectory of the Time Series Database for Financial Services market. Cloud-based deployment models have democratized access to high-performance databases, enabling even small and medium-sized enterprises to leverage sophisticated data management capabilities without significant upfront investments. The scalability, flexibility, and cost-efficiency offered by cloud solutions are attracting a diverse range of financial service providers, from traditional banks to innovative fintech startups. Furthermore, the integration of time series databases with big data platforms and machine learning tools is unlocking new opportunities for real-time analytics, personalized financial services, and predictive modeling, all of which contribute to the sustained expansion of the market.

From a regional perspective, North America continues to dominate the global Time Series Database for Financial Services market, accounting for the largest revenue share in 2024. This leadership position is attributed to the presence of major financial hubs, advanced IT infrastructure, and early adoption of cutting-edge technologies by leading banks and investment firms. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid digital transformation, increasing investments in fintech, and the rising adoption of cloud-based solutions in countries such as China, India, and Singapore. Europe is also witnessing substantial growth, supported by stringent regulatory frameworks and the increasing focus on data-driven financial services. Latin America and the Middle East & Africa are gradually catching up, with financial institutions in these regions investing in modern database solutions to enhance operational efficiency and customer experience.

In the evolving landscape of financial services, <a href="https://growthmarketreports.com/report/managed-temporal-services-market" target="_blank&

The small datasets for calculating the frequency of itemsets in transaction database contain Accidents, Chess, Connection, Mushroom, PUSBM, and Retail [32] transaction datasets. There are 500, 1000, 2000, and 5000 transactions per dataset. The small datasets for calculating the utility of itemsets in a transaction database contain Accidents, Chess, Connection, Mushroom, PUSBM, and Retail [32] transaction datasets. There are 500, 1000, 2000, and 5000 transactions per dataset. The large datasets for caluclating the frequency of itemsets in a transaction database contain Accidents, Connection, and PUSBM [32] datasets. There are 10000, 20000, 30000, and 50000 transactions per dataset. The large datasets for calculating the utility of itemsets in a transaction database contain Accidents, Connection, and PUSBM [32] transaction datasets. There are 10000, 20000, 30000, and 50000 transactions per dataset. (ZIP)

The Environmental Information Management System (EIM) is the Department of Ecology's main database for environmental monitoring data. EIM contains records on physical, chemical, and biological analyses and measurements. Supplementary information about the data (metadata) is also stored, including information about environmental studies, monitoring locations, and data quality. Data in EIM is collected by Ecology or on behalf of Ecology by environmental contractors - and by Ecology grant recipients, local governments, and volunteers. EIM Locations is a point feature service representing the monitoring locations from EIM. The locations consist of both surface locations for monitoring air, water, and habitat and wells for monitoring ground water. This feature service queries directly the EIM publication database which is updated nightly from the production transactional database.GIS Metadata: https://www.ecy.wa.gov/services/gis/data/environment/eimlocations.htm For more information, contact Christina Kellum, Washington State Department of Ecology GIS Manager, gis@ecy.wa.gov.

Database after computing the utility values of each item in transactions.

Managed FoundationDB Services Market Outlook

According to our latest research, the global managed FoundationDB services market size reached USD 1.23 billion in 2024 and is expected to grow at a robust CAGR of 18.2% during the forecast period, reaching USD 5.37 billion by 2033. The market’s impressive trajectory is primarily driven by the surging demand for scalable, high-performance, and fault-tolerant database solutions in mission-critical enterprise applications. This growth is further underpinned by increasing digital transformation initiatives, the proliferation of cloud-native architectures, and the need for reliable data management platforms across diverse industries.

One of the most significant growth factors for the managed FoundationDB services market is the rapid adoption of multi-model databases that can handle complex transactional workloads with ACID compliance. FoundationDB’s unique architecture, which supports multiple data models such as key-value, document, and graph, makes it a preferred choice for organizations seeking to consolidate disparate data sources and streamline their data infrastructure. Enterprises are increasingly leveraging managed services to offload the operational complexities of deployment, scaling, and maintenance, allowing them to focus on core business innovation. Additionally, the rise of real-time analytics and the need for high availability and disaster recovery capabilities in sectors such as BFSI and healthcare are further propelling the demand for managed FoundationDB services.

Another driving force behind market expansion is the growing emphasis on cloud adoption and hybrid IT environments. As businesses transition from legacy systems to modern cloud-based infrastructures, managed FoundationDB services offer seamless integration, scalability, and cost-efficiency. Cloud deployment models are particularly attractive to organizations aiming to reduce capital expenditures and enhance agility. Furthermore, the hybrid deployment trend is gaining momentum as enterprises seek to balance data sovereignty, regulatory compliance, and operational flexibility. Managed service providers are responding by offering tailored solutions that cater to diverse deployment preferences, ensuring optimal performance and security across cloud, on-premises, and hybrid environments.

The managed FoundationDB services market is also benefiting from an increased focus on data security, compliance, and support. With the tightening of data protection regulations such as GDPR and HIPAA, enterprises are prioritizing managed services that provide robust security features, regular updates, and proactive monitoring. The availability of comprehensive support and maintenance packages ensures minimal downtime and rapid issue resolution, which is critical for industries where data integrity and uptime are paramount. Moreover, the growing ecosystem of consulting and integration services is enabling organizations to maximize the value of their FoundationDB investments by aligning database capabilities with specific business objectives and technical requirements.

From a regional perspective, North America currently dominates the managed FoundationDB services market, accounting for the largest revenue share in 2024. This leadership is attributed to the presence of major technology companies, early adoption of advanced database technologies, and a mature managed services ecosystem. Europe follows closely, driven by stringent data compliance standards and a strong focus on digital transformation across industries such as finance, healthcare, and manufacturing. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by rapid IT infrastructure development, expanding e-commerce, and increasing investments in cloud computing. Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by growing enterprise digitization and favorable government initiatives.

Service Type Analysis

The managed FoundationDB services market is segmented by service type into consulting, deployment & integration, support & maintenance, and others. Consulting services play a pivotal role in helping organizations assess their data management needs, design optimal architectures, and formulate migration strategies from legacy databases to FoundationDB. As enterprises increasingly recognize the complexities involved in adopting multi-model databases, demand for expert consulting has surg

Database Monitoring Software Market Outlook

The global market size for database monitoring software was valued at approximately $2.5 billion in 2023 and is projected to grow to $5.7 billion by 2032, exhibiting a robust CAGR of 9.5% over the forecast period. The market is witnessing significant growth due to the increasing complexity of database environments and the need for real-time monitoring to ensure optimal performance and security of data assets.

One of the main factors driving the growth of the database monitoring software market is the rising adoption of cloud computing and big data analytics. Organizations are increasingly relying on cloud-based solutions to store and process vast amounts of data, necessitating advanced monitoring tools to manage these complex systems effectively. Additionally, the proliferation of big data initiatives has led to an increased demand for sophisticated database monitoring solutions that can handle high volumes of data while ensuring performance and compliance.

Another critical growth driver is the increasing importance of data security and compliance. With the growing number of cyber threats and stringent regulatory requirements, organizations are compelled to implement robust database monitoring solutions to safeguard sensitive information and ensure compliance with industry standards. These tools help in identifying potential vulnerabilities, detecting anomalies, and providing actionable insights to address security issues promptly, thereby minimizing the risk of data breaches and ensuring regulatory adherence.

The growing need for operational efficiency and cost reduction is also fueling the demand for database monitoring software. Companies are under constant pressure to optimize their operations and reduce costs, and database monitoring tools play a pivotal role in achieving these objectives. By providing real-time insights into database performance, these tools enable organizations to identify and resolve performance bottlenecks, optimize resource utilization, and enhance overall productivity. The ability to proactively manage and maintain databases ensures minimal downtime and improved service delivery, contributing to cost savings and increased efficiency.

In the financial services industry, Transaction Monitoring for Financial Services is becoming increasingly vital. As financial institutions handle a vast number of transactions daily, the need for real-time monitoring solutions that can detect suspicious activities and ensure compliance with regulations is paramount. These solutions help in identifying potential fraud, money laundering, and other financial crimes by analyzing transaction patterns and flagging anomalies. With the rise of digital banking and online transactions, financial institutions are investing heavily in transaction monitoring systems to protect their assets and maintain customer trust. The integration of advanced analytics and machine learning in these systems further enhances their ability to predict and prevent fraudulent activities, ensuring a secure financial environment.

Regionally, North America is expected to dominate the database monitoring software market over the forecast period, owing to the presence of a large number of technology-driven enterprises and early adoption of advanced monitoring solutions. The Asia Pacific region is anticipated to witness the highest growth rate due to the rapid digitization of businesses and increasing investments in IT infrastructure. Europe, Latin America, and the Middle East & Africa also present significant growth opportunities, driven by the rising awareness of data security and compliance, as well as the adoption of cloud-based solutions.

Component Analysis

The database monitoring software market can be segmented into software and services. The software segment includes various solutions designed to monitor and manage database performance, security, and compliance. These solutions offer features such as real-time monitoring, alerting, reporting, and analytics, enabling organizations to maintain optimal database performance and security. Market growth in this segment is driven by the increasing complexity of database environments and the need for advanced monitoring capabilities to ensure seamless operations.

Within the software segment, there are specialized tools catering to different types of databases, including SQL, NoSQL, and cloud databases. SQL monitor

Privacy is as a critical issue in the age of data. Organizations and corporations who publicly share their data always have a major concern that their sensitive information may be leaked or extracted by rivals or attackers using data miners. High-utility itemset mining (HUIM) is an extension to frequent itemset mining (FIM) which deals with business data in the form of transaction databases, data that is also in danger of being stolen. To deal with this, a number of privacy-preserving data mining (PPDM) techniques have been introduced. An important topic in PPDM in the recent years is privacy-preserving utility mining (PPUM). The goal of PPUM is to protect the sensitive information, such as sensitive high-utility itemsets, in transaction databases, and make them undiscoverable for data mining techniques. However, available PPUM methods do not consider the generalization of items in databases (categories, classes, groups, etc.). These algorithms only consider the items at a specialized level, leaving the item combinations at a higher level vulnerable to attacks. The insights gained from higher abstraction levels are somewhat more valuable than those from lower levels since they contain the outlines of the data. To address this issue, this work suggests two PPUM algorithms, namely MLHProtector and FMLHProtector, to operate at all abstraction levels in a transaction database to protect them from data mining algorithms. Empirical experiments showed that both algorithms successfully protect the itemsets from being compromised by attackers.

Privacy is as a critical issue in the age of data. Organizations and corporations who publicly share their data always have a major concern that their sensitive information may be leaked or extracted by rivals or attackers using data miners. High-utility itemset mining (HUIM) is an extension to frequent itemset mining (FIM) which deals with business data in the form of transaction databases, data that is also in danger of being stolen. To deal with this, a number of privacy-preserving data mining (PPDM) techniques have been introduced. An important topic in PPDM in the recent years is privacy-preserving utility mining (PPUM). The goal of PPUM is to protect the sensitive information, such as sensitive high-utility itemsets, in transaction databases, and make them undiscoverable for data mining techniques. However, available PPUM methods do not consider the generalization of items in databases (categories, classes, groups, etc.). These algorithms only consider the items at a specialized level, leaving the item combinations at a higher level vulnerable to attacks. The insights gained from higher abstraction levels are somewhat more valuable than those from lower levels since they contain the outlines of the data. To address this issue, this work suggests two PPUM algorithms, namely MLHProtector and FMLHProtector, to operate at all abstraction levels in a transaction database to protect them from data mining algorithms. Empirical experiments showed that both algorithms successfully protect the itemsets from being compromised by attackers.

Privacy is as a critical issue in the age of data. Organizations and corporations who publicly share their data always have a major concern that their sensitive information may be leaked or extracted by rivals or attackers using data miners. High-utility itemset mining (HUIM) is an extension to frequent itemset mining (FIM) which deals with business data in the form of transaction databases, data that is also in danger of being stolen. To deal with this, a number of privacy-preserving data mining (PPDM) techniques have been introduced. An important topic in PPDM in the recent years is privacy-preserving utility mining (PPUM). The goal of PPUM is to protect the sensitive information, such as sensitive high-utility itemsets, in transaction databases, and make them undiscoverable for data mining techniques. However, available PPUM methods do not consider the generalization of items in databases (categories, classes, groups, etc.). These algorithms only consider the items at a specialized level, leaving the item combinations at a higher level vulnerable to attacks. The insights gained from higher abstraction levels are somewhat more valuable than those from lower levels since they contain the outlines of the data. To address this issue, this work suggests two PPUM algorithms, namely MLHProtector and FMLHProtector, to operate at all abstraction levels in a transaction database to protect them from data mining algorithms. Empirical experiments showed that both algorithms successfully protect the itemsets from being compromised by attackers.

Average values and confidence intervals at 95% for the service measures in each trajectory or classa.

Characteristics of the three service trajectories or classesa.

Demographic and clinical characteristics of patients in the final sample (N = 2 333).