Cross-platform federated analytics system for secure and privacy-compliant advertising intelligence.

Abstract

A cross-platform integrated analytics system (100) for secure and privacy-compliant advertising intelligence, consisting of: a) a multitude of composite analytics agents distributed across heterogeneous advertising platforms, each composite analytics agent being configured to capture, normalize and process platform-specific advertising data locally without exporting raw data outside the respective platform boundary; b) a local analytics and feature extraction module that is operationally coupled with each federated analytics agent and configured to calculate advertising performance metrics, attribution signals, and predictive indicators from locally stored data; c) a privacy protection and secure computation module configured to apply at least one of the following techniques to locally computed analysis results: differential privacy, secure multi-party computation or homomorphic encryption; d) a federated aggregation and coordination module configured to securely aggregate protected analytics parameters received from the multitude of advertising platforms without accessing the underlying raw data; e) a cross-platform intelligence modeling module configured to generate consistent insights into advertising intelligence from the aggregated parameters; and f) a module for the secure delivery of intelligence, configured to deliver aggregated, non-identifiable advertising insights to authorized entities, g) where the system enables cross-platform generation of advertising intelligence while ensuring data sovereignty, user privacy and compliance with legal regulations.

Description

[0001] The present invention relates to secure data analysis and digital advertising information systems. More precisely, it is a cross-platform integrated analytics framework that enables the data protection-compliant collection, processing, and analysis of advertising performance data across heterogeneous digital platforms. The invention ensures the creation of advertising information in compliance with legal regulations, without the direct transfer or centralization of sensitive user- or platform-specific data.

[0002] In the digital advertising ecosystem, companies rely heavily on cross-platform data analytics to understand campaign performance, audience behavior, and return on investment. However, traditional ad intelligence systems require centralized aggregation of user-level and platform-specific data, leading to significant privacy risks, regulatory violations, and the disclosure of sensitive business information. Stringent data protection regulations and platform-specific data silos further limit the ability to perform unified analysis, resulting in fragmented insights and less accurate decision-making.

[0003] Existing approaches attempting to address these challenges often rely on anonymization, data masking, or third-party providers, which are increasingly inadequate given the risks of re-identification and do not completely eliminate the reliance on trust. Furthermore, differing data schemas, access controls, and analytics capabilities make it difficult to efficiently deploy these methods across heterogeneous platforms such as web, mobile, connected TV, and social media. As a result, advertisers face delays, incomplete information, and compromised data integrity when optimizing campaigns across platforms.

[0004] Accordingly, there is a need for a problem-solving analytics architecture that enables the collaborative generation of advertising information without direct data exchange or centralized data storage. A privacy-compliant, federated analytics system that can be operated across platforms while ensuring data sovereignty, compliance with legal regulations, and secure data processing would overcome these limitations. Such a solution would allow advertisers and platforms to gain accurate, real-time insights while guaranteeing user privacy, platform confidentiality, and cross-platform interoperability.

[0005] One objective of this disclosure is to provide a cross-platform federated analytics system that enables secure and privacy-compliant advertising intelligence without the need to centrally collect raw user data or platform-specific data. The system ensures data sovereignty by keeping sensitive information within the original platforms.

[0006] Another objective of the present disclosure is to enable the uniform generation of advertising intelligence across heterogeneous digital advertising platforms. The invention solves problems of data fragmentation by supporting federated aggregation and standardized analyses without impairing the autonomy of the platforms.

[0007] Another objective of this disclosure is to protect user privacy and proprietary platform data through advanced, privacy-enhancing computational techniques. The system uses differential privacy, encryption, and secure multi-party computation to prevent data leaks and inference attacks. This objective strengthens trust between participating platforms and advertisers.

[0008] Another objective of the present disclosure is to ensure compliance with global data protection and advertising regulations. The invention comprises mechanisms for policy management, consent enforcement, and jurisdiction-specific data processing. This enables advertisers to gain insights while simultaneously complying with evolving regulatory frameworks.

[0009] Another objective of this disclosure is to improve the accuracy and reliability of ad attribution and performance measurement. By using federated information models, the system overcomes the limitations of isolated analyses. This objective enables data-driven campaign optimization across different platforms.

[0010] Another objective of this disclosure is to support scalable and adaptive analytics in dynamically changing advertising ecosystems. The system is designed to seamlessly integrate new platforms, formats, and data sources. This ensures long-term operational efficiency and extensibility.

[0011] Another objective of this disclosure is to enable the secure and controlled provision of advertising insights to authorized stakeholders. The system enforces role-based access control and auditability for all intelligence results.

[0012] Another goal of this disclosure is to continuously optimize advertising intelligence through adaptive learning mechanisms. The system includes feedback loops to refine local and federated models over time.

[0013] Further objectives and benefits of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0014] The present invention relates to a cross-platform, integrated analytics system developed for generating secure and privacy-compliant advertising information. The system enables joint analysis across multiple digital advertising platforms without the need for centralized raw data storage. This ensures compliance with data protection regulations while simultaneously guaranteeing the accuracy of the analyses.

[0015] Another embodiment of the present invention is the use of federated analytics agents within individual advertising platforms. These agents process platform-specific data locally and extract relevant advertising information. The raw user-level data remains restricted to the original platform at all times.

[0016] Another embodiment of the present invention is the implementation of local analysis and feature extraction mechanisms. These mechanisms calculate performance indicators, attribution signals, and predictive indicators within the platform boundaries. Only protected analysis parameters are passed on for federated aggregation.

[0017] Another embodiment of the present invention involves the integration of advanced, privacy-compliant computing techniques. The system employs differential privacy, secure multi-party computing, and encryption to prevent data leaks. This embodiment ensures protection against re-identification and inference attacks.

[0018] Another embodiment of the present invention is the federated aggregation and coordination framework. This framework securely combines protected analysis results from multiple platforms without accessing raw data. This enables the creation of accurate cross-platform information.

[0019] Another embodiment of the present invention is the ability to perform cross-platform information modeling. The system generates uniform attribution models, performance benchmarks, and optimization insights. These models dynamically adapt to platform diversity and data availability.

[0020] Another embodiment of the present invention involves the integration of controls for policy compliance and governance. The system enforces consent management, legally compliant data processing, and the logging of audits. This ensures transparency and regulatory compliance throughout the entire analysis cycle.

[0021] Another embodiment of the present invention is the secure provision and continuous optimization of advertising information. The insights are provided via controlled dashboards and APIs with role-based access. Adaptive learning mechanisms continuously refine the accuracy of the analyses while simultaneously ensuring privacy protection.

[0022] The present invention relates to a cross-platform federated analytics system (100) for secure and privacy-compliant advertising intelligence, designed to enable unified advertising insights across multiple digital platforms without centralized data sharing. The system is structured as modular components, including federated analytics agents, local analytics and feature extraction modules, privacy-compliant computation modules, federated aggregation and coordination modules, and cross-platform intelligence modeling modules. Each module operates within defined security and governance boundaries to ensure data sovereignty and compliance with legal regulations. Together, the modules enable the secure aggregation of protected analytics parameters while preventing the disclosure of raw user- or platform-specific data.Through secure information provision and adaptive learning modules, the invention ensures a scalable, accurate and continuously optimized generation of advertising information across heterogeneous advertising ecosystems.

[0023] Module for the interface to federated data sources: This module runs on each participating advertising platform and is responsible for the secure connection to native data sources such as impression logs, click events, conversion metrics, and audience segmentation data. The module standardizes heterogeneous data schemas into a federation-compatible representation while simultaneously enforcing local data governance policies. At no point is raw or identifiable data transferred outside the source platform, thus preserving the platform's data sovereignty and confidentiality.

[0024] Local Analytics and Feature Extraction Module: The local analytics module performs platform-specific calculations on standardized data to extract relevant advertising information, including performance metrics, interaction indicators, attribution signals, and temporal trends. These calculations are performed entirely within the platform's boundaries using configurable analytical models. The module outputs only aggregated, encrypted, or noise-treated parameters suitable for federated collaboration, thus ensuring compliance with data privacy regulations.

[0025] Privacy and Secure Computing Module: This module applies advanced privacy protection techniques, such as differential privacy, secure multi-party computation, and homomorphic encryption, to locally computed analytics results. It ensures that shared parameters cannot be reverse-engineered to infer individual user behavior or proprietary platform data. The module dynamically adjusts privacy budgets and security parameters based on regulatory requirements and campaign sensitivity.

[0026] Federated Aggregation and Coordination Module: The federated aggregation module coordinates the secure collection and combination of protected analysis parameters from multiple platforms. As a neutral coordinator, it performs encrypted aggregation, weighted averaging, or secure statistical fusion without accessing raw data. This module enables cross-platform intelligence synthesis while strictly isolating individual platform datasets.

[0027] Module for cross-platform information modeling: This module generates unified advertising information models using aggregated federated parameters. It performs cross-platform attribution analyses, performance benchmarking, reach and frequency estimations, and predictive optimizations. The models are designed to adapt to varying platform contributions and data availability, thus ensuring robust and unbiased information generation across different advertising ecosystems.

[0028] Compliance and Governance Module: The governance module ensures compliance with legal, contractual, and organizational regulations throughout the entire analytics lifecycle. It manages consent signals, data protection regulations, audit logs, and access controls. The module guarantees adherence to global data protection policies while ensuring transparency and traceability of all related analytics processes.

[0029] Module for secure information delivery and visualization: This module provides authorized stakeholders with actionable advertising insights via secure dashboards, reports, or machine-readable APIs. The insights are presented in an aggregated and non-identifiable format, enabling campaign optimization decisions without disclosing sensitive data. Role-based access controls ensure that only authorized users can view or utilize specific information.

[0030] Adaptive Learning and Optimization Module: The adaptive learning module continuously refines local and federated analytics models based on feedback, campaign results, and evolving platform dynamics. It supports incremental updates without retraining on centralized data, thus improving information accuracy over time while maintaining privacy. This module enables the system to autonomously adapt to new platforms, formats, and regulatory requirements.

[0031] Together, these modules form a robust, privacy-compliant federated analytics architecture that enables secure, compliant, and scalable cross-platform generation of advertising information without centralized data sharing.

[0032] The invention is explained again below with reference to the figure. This shows: Fig.A block diagram of a cross-platform federated analytics system (100) with interconnected modules for the secure, privacy-compliant generation of advertising information. The figure shows the sequential and feedback-based interaction between the modules for federated data source interfaces, analytics, privacy, aggregation, modeling, governance, and information delivery.

[0033] In operation, the cross-platform federated analytics system (100) is initiated by deploying a federated analytics agent on each participating advertising platform, integrating the agent into native data pipelines, event logs, and campaign management systems. Raw advertising data such as impressions, clicks, conversions, bid values, and audience interactions are continuously collected and processed locally. Within the platform boundaries, the system performs schema normalization and feature engineering, followed by the execution of local analytics models to calculate performance metrics, attribution signals, and predictive indicators. These locally derived results are then transformed using cryptographic safeguards and privacy protection mechanisms to ensure that no user-identifying or platform-sensitive information is exposed outside the local environment.

[0034] The protected analysis parameters are then transmitted via secure communication channels to a federated coordination layer, which manages the synchronization, versioning, and weighting of contributions across platforms. This coordination layer executes secure aggregation protocols that enable combined statistical analysis and cross-platform intelligence gathering without decrypting the contributions of individual platforms. Advanced federated computational techniques ensure resilience against inference attacks and data leaks while maintaining consistent analytical accuracy. The system dynamically adapts the aggregation logic to platform availability, data freshness, and campaign objectives, thus ensuring consistent and reliable intelligence results in heterogeneous ecosystems.

[0035] Finally, the aggregated federated information is processed by centralized modeling components that generate actionable advertising insights, including cross-channel performance comparisons, unified attribution models, and optimization recommendations. These insights are delivered via secure visualization dashboards or APIs with strict role-based access controls and audit logging. The system also incorporates continuous learning mechanisms, where feedback signals and outcome metrics are fed back to local analytics agents to refine future calculations. This closed, privacy-focused workflow enables technically robust, scalable, and compliant generation of advertising information across multiple platforms without requiring centralized data disclosure.

Claims

[1] A cross-platform integrated analytics system (100) for secure and privacy-compliant advertising intelligence, consisting of: a) a multitude of composite analytics agents distributed across heterogeneous advertising platforms, each composite analytics agent being configured to capture, normalize and process platform-specific advertising data locally without exporting raw data outside the respective platform boundary; b) a local analytics and feature extraction module that is operationally coupled with each federated analytics agent and configured to calculate advertising performance metrics, attribution signals, and predictive indicators from locally stored data; c) a privacy protection and secure computation module configured to apply at least one of the following techniques to locally computed analysis results: differential privacy, secure multi-party computation or homomorphic encryption; d) a federated aggregation and coordination module configured to securely aggregate protected analytics parameters received from the multitude of advertising platforms without accessing the underlying raw data; e) a cross-platform intelligence modeling module configured to generate consistent insights into advertising intelligence from the aggregated parameters; and f) a module for the secure delivery of intelligence, configured to deliver aggregated, non-identifiable advertising insights to authorized entities, g) where the system enables cross-platform generation of advertising intelligence while ensuring data sovereignty, user privacy and compliance with legal regulations. [2] System (100) according to claim 1, wherein the connected analysis agents are configured to perform schema harmonization across different advertising platforms, including web, mobile, social media and networked television ecosystems. [3] System (100) according to claim 1, wherein the local analysis and feature extraction module executes local machine learning models to generate campaign-level performance predictions without transferring model training data externally. [4] System (100) according to claim 1, wherein the privacy-preserving and secure calculation module dynamically adjusts the data protection budgets based on legal jurisdiction, campaign sensitivity or guidelines set by the advertiser. [5] System (100) according to claim 1, wherein the federated aggregation and coordination module performs encrypted aggregation using secure statistical fusion techniques that are resistant to inference and reconstruction attacks. [6] System (100) according to claim 1, wherein the cross-platform information modeling module generates uniform attribution models, reach and frequency estimates, and cross-channel optimization recommendations. [7] System (100) according to claim 1, further comprising a policy compliance and governance module configured to enforce consent management, access control, audit logging and data handling rules in accordance with the applicable legal system. [8] System (100) according to claim 1, wherein the module provides secure information provisioning insights via role-based dashboards, reports or application programming interfaces while preventing the disclosure of platform-specific data. [9] System (100) according to claim 1, further comprising an adaptive learning module configured to iteratively refine local and federated analysis models based on feedback signals and campaign outcome data. [10] System (100) according to claim 1, wherein the federated analysis system operates without centralized storage of advertising data at the user level and thereby ensures the protection of privacy and confidentiality of the platform by design.

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