In the publication [1] we implemented anonymization and synthetization techniques for a structured data set, which was collected during the HiGHmed Use Case Cardiology study [2]. We employed the data anonymization tool ARX [3] and the data synthetization framework ASyH [4] individually and in combination. We evaluated the utility and shortcomings of the different approaches by statistical analyses and privacy risk assessments. Data utility was assessed by computing two heart failure risk scores (Barcelona BioHF [5] and MAGGIC [6]) on the protected data sets. We observed only minimal deviations to scores from the original data set. Additionally, we performed a re-identification risk analysis and found only minor residual risks for common types of privacy threats. We could demonstrate that anonymization and synthetization methods protect privacy while retaining data utility for heart failure risk assessment. Both approaches and a combination thereof introduce only minimal deviations from the original data set over all features. While data synthesis techniques produce any number of new records, data anonymization techniques offer more formal privacy guarantees. Consequently, data synthesis on anonymized data further enhances privacy protection with little impacting data utility. We hereby share all generated data sets with the scientific community through a use and access agreement. [1] Johann TI, Otte K, Prasser F, Dieterich C: Anonymize or synthesize? Privacy-preserving methods for heart failure score analytics. Eur Heart J 2024;. doi://10.1093/ehjdh/ztae083 [2] Sommer KK, Amr A, Bavendiek, Beierle F, Brunecker P, Dathe H et al. Structured, harmonized, and interoperable integration of clinical routine data to compute heart failure risk scores. Life (Basel) 2022;12:749. [3] Prasser F, Eicher J, Spengler H, Bild R, Kuhn KA. Flexible data anonymization using ARX—current status and challenges ahead. Softw Pract Exper 2020;50:1277–1304. [4] Johann TI, Wilhelmi H. ASyH—anonymous synthesizer for health data, GitHub, 2023. Available at: https://github.com/dieterich-lab/ASyH. [5] Lupón J, de Antonio M, Vila J, Peñafiel J, Galán A, Zamora E, et al. Development of a novel heart failure risk tool: the Barcelona bio-heart failure risk calculator (BCN Bio-HF calculator). PLoS One 2014;9:e85466. [6] Pocock SJ, Ariti CA, McMurray JJV, Maggioni A, Køber L, Squire IB, et al. Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. Eur Heart J 2013;34:1404–1413.

Differential-Privacy as-a-Service Market Outlook

According to our latest research, the global Differential-Privacy as-a-Service market size reached USD 1.42 billion in 2024, reflecting robust adoption across industries. The market is projected to expand at a CAGR of 28.7% during the forecast period, reaching USD 11.87 billion by 2033. This remarkable growth trajectory is primarily driven by the escalating demand for privacy-preserving data analytics, stricter regulatory frameworks, and increasing reliance on cloud-based solutions. Enterprises worldwide are prioritizing data privacy and compliance, making Differential-Privacy as-a-Service a cornerstone of modern data management strategies.

The growth of the Differential-Privacy as-a-Service market is significantly fueled by the heightened awareness and enforcement of data privacy regulations such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and other global privacy mandates. Organizations are under immense pressure to ensure that sensitive information remains confidential while still leveraging data for insights and innovation. Differential privacy, by introducing mathematical noise to datasets, enables businesses to analyze data without exposing individual identities. This capability is particularly relevant in sectors like healthcare, finance, and government, where the stakes of data breaches are exceptionally high. The need to balance data utility with privacy is pushing organizations to adopt Differential-Privacy as-a-Service solutions, which offer scalable, compliant, and efficient ways to mitigate privacy risks.

Another major growth factor is the rapid digital transformation and proliferation of big data analytics across industries. As enterprises accumulate vast volumes of personal and sensitive data, the risk of privacy breaches increases exponentially. Differential-Privacy as-a-Service platforms address this challenge by providing robust privacy guarantees, enabling organizations to harness the power of data analytics without compromising on privacy. The rise of artificial intelligence and machine learning applications further accentuates the need for privacy-preserving technologies, as these systems often require access to large, diverse datasets. By integrating differential privacy into their data workflows, businesses can foster innovation while maintaining customer trust and regulatory compliance.

Furthermore, the increasing adoption of cloud-based infrastructure and the shift towards remote and hybrid work environments have amplified the demand for scalable privacy solutions. Cloud deployment of Differential-Privacy as-a-Service offers flexibility, cost efficiency, and ease of integration with existing data ecosystems. Enterprises, regardless of size, can now access advanced privacy tools without significant upfront investment in infrastructure or specialized expertise. This democratization of privacy technology is particularly beneficial for small and medium enterprises (SMEs), which often lack the resources to develop in-house privacy solutions. As cloud adoption continues to surge, so does the uptake of Differential-Privacy as-a-Service, positioning it as a critical enabler of secure digital transformation.

From a regional perspective, North America remains the dominant market, owing to the presence of leading technology providers, stringent data protection regulations, and high digital maturity. Europe follows closely, driven by rigorous privacy standards and a proactive regulatory landscape. The Asia Pacific region is witnessing rapid growth, fueled by expanding digital economies, increasing awareness of data privacy, and government-led initiatives to enhance cybersecurity. Latin America and the Middle East & Africa are also experiencing steady adoption, as businesses in these regions modernize their data management practices and seek to comply with emerging privacy laws. Overall, the global landscape for Differential-Privacy as-a-Service is characterized by robust growth, technological innovation, and a strong emphasis on regulatory compliance.

Component Analysis

The Differential-Privacy as-a-Service market is segmented by component into software and services, each playing a pivotal role in the ecosystem. The software segment encompasses standalone privacy-preserving analytics platforms, APIs, and SDKs that enable seamless integration of differential privacy into existin

Privacy-preserving record linkage (PPRL) technology, crucial for linking records across datasets while maintaining privacy, is susceptible to graph-based re-identification attacks. These attacks compromise privacy and pose significant risks, such as identity theft and financial fraud. This study proposes a zero-relationship encoding scheme that minimizes the linkage between source and encoded records to enhance PPRL systems’ resistance to re-identification attacks. Our method’s efficacy was validated through simulations on the Titanic and North Carolina Voter Records (NCVR) datasets, demonstrating a substantial reduction in re-identification rates. Security analysis confirms that our zero-relationship encoding effectively preserves privacy against graph-based re-identification threats, improving PPRL technology’s security.

Secure Multi-Party Computation Health-Analytics Market Outlook

According to our latest research, the global Secure Multi-Party Computation Health-Analytics market size reached USD 1.47 billion in 2024, reflecting a robust surge in adoption across the healthcare industry. The market is expected to grow at a remarkable CAGR of 22.3% from 2025 to 2033, projecting a value of USD 11.47 billion by 2033. This exponential growth is primarily driven by the increasing need for privacy-preserving data analytics in healthcare, the proliferation of sensitive patient data, and the mounting regulatory pressures surrounding data security and compliance.

The rapid digitization of healthcare systems globally is a fundamental growth factor for the Secure Multi-Party Computation (SMPC) Health-Analytics market. As electronic health records (EHRs), telemedicine, and connected medical devices become ubiquitous, the volume of health data generated has increased exponentially. This data explosion, while valuable for analytics and research, introduces significant privacy and security risks. SMPC technology enables multiple parties to jointly analyze and compute over sensitive data sets without exposing the underlying data to each other, thereby facilitating collaborative research and analytics while maintaining strict data privacy. Healthcare organizations are increasingly recognizing the importance of leveraging SMPC to unlock insights from distributed data sources, such as cross-institutional medical research and population health studies, without violating patient confidentiality or regulatory mandates. This growing awareness is catalyzing the adoption of SMPC-driven analytics solutions across the healthcare value chain.

Another critical driver is the tightening regulatory landscape governing health data privacy and cross-border data sharing. Regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, General Data Protection Regulation (GDPR) in Europe, and similar frameworks worldwide, impose stringent requirements on how patient data is accessed, shared, and processed. Non-compliance can result in severe penalties, reputational damage, and operational disruptions. Secure Multi-Party Computation Health-Analytics platforms provide healthcare stakeholders with the technological means to comply with these regulations while still enabling valuable data-driven insights. By ensuring that sensitive data remains encrypted and inaccessible during computation, SMPC solutions minimize the risk of data breaches and unauthorized disclosures, which is a significant consideration for healthcare providers, payers, and research institutions alike.

The growing demand for advanced healthcare analytics and precision medicine is also fueling the expansion of the SMPC Health-Analytics market. As healthcare moves toward more personalized treatment paradigms, there is a heightened need to aggregate and analyze diverse data sets, including genomics, medical imaging, and real-world evidence, from multiple sources. SMPC enables secure collaboration between pharmaceutical companies, hospitals, and research centers, facilitating breakthroughs in drug discovery, clinical trials, and epidemiological research. Furthermore, the ongoing advancements in cryptographic algorithms, increased computational power, and integration with artificial intelligence (AI) and machine learning (ML) are making SMPC solutions more scalable, practical, and cost-effective for large-scale healthcare applications. These technological advancements are expected to further accelerate market growth over the forecast period.

From a regional perspective, North America currently dominates the Secure Multi-Party Computation Health-Analytics market, accounting for the largest share in 2024, driven by the presence of leading healthcare institutions, robust regulatory frameworks, and early adoption of advanced analytics technologies. Europe follows closely, benefiting from strong data privacy regulations and significant investment in healthcare IT infrastructure. The Asia Pacific region is anticipated to witness the fastest growth, with a projected CAGR of over 25% during the forecast period, fueled by expanding healthcare digitization, government initiatives, and increasing focus on data security. Latin America and the Middle East & Africa are also expected to experience steady growth, supported by rising healthcare expenditures and the gradual adoption of privacy-preserving analytics solutions.

Differential Privacy Software Market Outlook

According to our latest research, the global Differential Privacy Software market size reached USD 1.12 billion in 2024, reflecting robust adoption across industries prioritizing data privacy and regulatory compliance. With a compound annual growth rate (CAGR) of 28.7% from 2025 to 2033, the market is forecasted to reach an impressive USD 9.67 billion by 2033. This remarkable growth trajectory is primarily fueled by intensifying data privacy regulations, the proliferation of big data analytics, and the increasing reliance on advanced machine learning models. As organizations globally strive to balance data utility and privacy, the demand for differential privacy software is accelerating, making it a pivotal technology in the modern data protection landscape.

The most significant growth driver for the Differential Privacy Software market is the escalating regulatory environment around data privacy and security. Legislations such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and similar frameworks in Asia Pacific and Latin America are compelling organizations to adopt privacy-enhancing technologies. Differential privacy, which mathematically guarantees individual privacy in data analytics, is increasingly viewed as a critical compliance tool. Enterprises across sectors are integrating differential privacy solutions to enable secure data sharing and analytics while minimizing the risk of personal data exposure. This regulatory-driven adoption is expected to remain a key growth catalyst, particularly as enforcement actions and penalties for non-compliance become more prevalent and severe.

Another substantial growth factor is the surge in big data and machine learning applications, which necessitate robust privacy-preserving mechanisms. As organizations leverage advanced analytics and AI to drive business insights, the risk of re-identifying individuals from anonymized datasets has become a significant concern. Differential privacy software addresses this challenge by introducing mathematically rigorous noise into data queries, ensuring that individual records cannot be reverse-engineered. This capability is especially critical for industries such as healthcare, finance, and retail, where sensitive personal information is routinely processed. The increasing sophistication of cyber threats and growing awareness among enterprises about the limitations of traditional anonymization techniques further underscore the importance of adopting differential privacy solutions.

Additionally, the expanding ecosystem of cloud computing and the shift towards decentralized data architectures are contributing to the market’s robust growth. Organizations are increasingly migrating their data infrastructure to the cloud, seeking scalable and flexible privacy solutions that can be seamlessly integrated into diverse environments. Differential privacy software vendors are responding by offering cloud-native and hybrid deployment options, enabling enterprises to protect data across on-premises and cloud-based systems. This trend is particularly pronounced among large enterprises with complex, distributed data landscapes, but is also gaining traction among small and medium enterprises seeking cost-effective privacy solutions. The ongoing digital transformation across industries is expected to sustain high demand for differential privacy software in the coming years.

From a regional perspective, North America currently dominates the Differential Privacy Software market, accounting for the largest revenue share in 2024. This leadership position is attributed to the region’s advanced technology ecosystem, early adoption of privacy-enhancing technologies, and stringent regulatory frameworks. Europe follows closely, driven by GDPR compliance requirements and growing investments in data security. Meanwhile, the Asia Pacific region is emerging as a high-growth market, supported by rapid digitalization, expanding BFSI and healthcare sectors, and increasing government initiatives to strengthen cyber resilience. As organizations worldwide prioritize data privacy as a strategic imperative, the differential privacy software market is set to witness dynamic growth across all major regions.

Federated Analytics Platform Market Outlook

According to our latest research, the global federated analytics platform market size reached USD 1.29 billion in 2024, driven by the increasing demand for privacy-preserving analytics and decentralized data processing. The market is expected to grow at a robust CAGR of 18.7% from 2025 to 2033, ultimately reaching a forecasted value of USD 6.19 billion by 2033. This rapid growth is primarily fueled by the heightened adoption of federated learning technologies across critical sectors such as healthcare, finance, and government, where data privacy and compliance are paramount.

The primary growth factor for the federated analytics platform market is the rising global emphasis on data privacy and regulatory compliance. With regulations such as GDPR, HIPAA, and CCPA becoming increasingly stringent, organizations are seeking solutions that allow them to extract valuable insights from distributed data sources without compromising on privacy. Federated analytics platforms enable collaborative data analysis by allowing data to remain at its source, thus minimizing the risk of data breaches and ensuring compliance with regional and industry-specific regulations. This privacy-centric approach is particularly appealing to sectors handling sensitive information, such as healthcare and finance, where the need for secure and compliant analytics solutions is acute.

Another significant driver is the exponential growth in data generation across distributed environments, including edge devices, IoT networks, and multi-cloud infrastructures. Traditional centralized analytics models struggle to keep pace with the volume, velocity, and variety of modern data streams, leading to latency issues and increased security risks. Federated analytics platforms address these challenges by enabling real-time, decentralized data processing and analysis. This approach not only enhances operational efficiency but also supports more agile and scalable analytics workflows, empowering organizations to derive actionable insights from a broader array of data sources while maintaining control over their data assets.

Furthermore, the ongoing digital transformation across industries has amplified the need for advanced analytics capabilities that are both scalable and secure. As businesses invest in AI-driven decision-making and predictive analytics, federated analytics platforms are becoming indispensable tools for unlocking value from siloed data without violating privacy norms. The integration of AI and machine learning with federated analytics is enabling organizations to build more robust, accurate, and context-aware models, further accelerating the adoption of these platforms. The synergy between federated analytics and emerging technologies such as blockchain, secure multi-party computation, and homomorphic encryption is also opening new avenues for innovation and market expansion.

From a regional perspective, North America continues to dominate the federated analytics platform market, accounting for the highest revenue share in 2024. This leadership is attributed to the region’s advanced IT infrastructure, strong regulatory frameworks, and early adoption of privacy-enhancing technologies. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid digitalization, increasing investments in AI and analytics, and evolving data privacy regulations. Europe also remains a significant market, driven by strict compliance requirements and the widespread adoption of federated analytics in sectors such as healthcare and finance. Latin America and the Middle East & Africa are witnessing steady growth, supported by rising awareness and gradual technological advancements.

Component Analysis

The federated analytics platform market by component is broadly segmented into software and services. The software segment encompasses the core platforms and tools that facilitate federated data analysis, including data orchestration, model training, and privacy-preserving computation modules. In 2024, the software segment accounted for the largest share of the market, driven by continuous advancements in federated learning algorithms, user-friendly interface development, and seamless integration with existing enterprise analytics infrastructures. The demand for robust, scalable, and secure federated analytics software is rising sharply as organizations seek to unlock value from

pone.0314656.t002 - Privacy-preserving method for face recognition based on homomorphic encryption