dataesr/scientific-paragraphs-categorization huggingface dataset:
I. INTRODUCTION Several technologies and tools exist in literature to allow for privacy-preserving data processing. However, they are not largely used yet in application domains due to some limitations and constraints. Fully homomorphic encryption, for example, despite being versatile in allowing various computations over federated sets of encrypted data, it suffers from a significant performance degradation as the amount of data to be processed increases, while complex calculations in large-scale deployments take a significant toll when multi-party computation methods are used. Another reason for the lack of uptake of such technologies is related to their user-friendliness, both for researchers and service providers, as well as for data owners. The type and configuration of the privacy-preserving technology to be used, as well as the level of privacy required for a given dataset and a given output, is often unclear to all parties involved.
The ENCRYPT project goes beyond the state-of-the-art to overcome the limitations of these privacy-preserving technologies in several aspects. First of all, it addresses the scalability issue by going beyond the single-key FHE paradigm and explore the application and the practicability of new multikey and threshold FHE schemes especially in a federated context. Second, to address the drawbacks of each technology in terms of the covered cybersecurity threats and performance, ENCRYPT investigates the combinations of several of these privacy-preserving methods. Third, ENCRYPT addresses the slow computation times associated with the existing solutions for privacy-preserving technologies based on HE or SMPC, by providing hardware acceleration in a user-friendly way. Fourth, ENCRYPT looks at the necessary methods in order to make these advanced privacy-preserving data processing technologies more suitable to interoperate with existing infrastructures and traditional security mechanisms.
A. Medical Use Case Cooperative oncology involves different specialists from different medical disciplines evaluating and analyzing the same patient from different perspectives. This leads to large amounts of medical data being shared in real time and across different hospitals, leading to data protection and privacy preservation issues. Patients are registered to the hospital system, clinical data are collected and imaging procedures are performed. Once the images and the clinical data have been evaluated, the physician compiles a report. The physician might need a consultation with a colleague for a second opinion, or a consultation among different specialists might be useful to determine the best therapeutic option for the patient. The different professional figures involved may not be in the same hospital/institution.
Similarly, in Radiotherapy Unit, different professional figures have access to patient data and imaging files. Constant data exchange is essential to plan the treatment and verify that the scheduled treatment is appropriately delivered. Personal and sensitive data are currently accessible to all the health-care professionals and researchers involved. At the state of the art, data protection measures include removing sensitive data by implementing a non-standardized anonymization procedure. Moreover, currently the only available option to share patients' data and images is data transfer, considering that hospital systems are not accessible by external subjects. The ENCRYPT platform will allow radiologists to protect data communication between different health care professionals in other departments/hospitals, while also preserving data integrity and patient privacy.
The ENCRYPT framework is expected to enable the processing of the data prior to the sharing with different parties, ensuring the preservation of data confidentiality and integrity, and compliance with data protection law. This will permit the use of the data in the subsequent steps of the diagnostic/therapeutic pathway and during patient's follow-up consultations. In the Radiotherapy setting, using the ENCRYPT platform, the integrity of treatment planning data and images metadata will be ensured. Moreover, all the data shared to any other stakeholder through this service/tool will comply with GDPR, without the risk that such data can lead to the actual identification of a real patient.
B. Fintech Use case Just like any other emerging technological domains, data security and privacy preservation mechanisms ensuring the anonymity of the sensitive data are of paramount importance to the Fintech domain as well. In the ENCRYPT's Fintech use case, two main challenges will be tackled during the project's pilot activities, implemented in two different sub-use cases. Sub-use case 1 -Security and Impact Assessment of data owned by financial institutions: The first Fintech sub-use case tackles the need of the financial institutions and banks to be sure about the security and privacy levels required to ensure the anonymity of their clients not only internally to their organization, but also when they share their data to potential 3rd parties to perform data analysis and/or to deliver tailored software solutions for the bank's activities.
Moreover, in case these data are either processed internally or shared to external 3rd parties for further processing, compliance only to GDPR currently applied in these organizations is not enough, since information linked with a client might potentially lead to the actual identification of person or/and sensitive information linked with that person. In this scenario the DPO of EPIBANK will exploit the functionalities of the ENCRYPT recommendation system, where it will be informed about the type of data owned by its organization and their level of security and privacy. The DPO will be also informed about the potential security impact it might occur to its organization in case there is a security issue on its owned data. Additionally, the ENCRYPT recommendation system will inform the DPO about the available privacy-preserving technologies that can be applied to its use case presenting also the potential trade-offs in terms of system's performance.
Finally, the overall ENCRYPT platform will provide the option to select one of the ENCRYPT's privacypreserving method to be applied to the EPIBANK's data to be shared with external 3rd party entities so that they can perform data processing and deliver to the EPIBANK tailored software solutions that will help the bank on its dept collection policies. Sub-use case 2 -Training of tailored-made AI models with data shared by financial organization to 3rd parties: The second sub-use case describes the situation when the 3rd party/entity receives these data from the financial institutions/banks and wishes to perform AI-driven data analysis in order to deliver tailored software solutions serving the strategies and policies of the bank in specific business portfolios. More specifically, in the ENCRYPT project EXUS, as an AI-software house delivering solutions to financial organizations to manage their debt collection portfolio, will develop tailored AI-models using the EPIBANK's clients data in order to perform among others client stratification, behaviour forecasting and overall scoring of the bank agents responsible for handling each client.
Since these functionalities are tailored specifically to the needs of EPIBANK, clients' historical data over a long period of 0. 5-1 year have to be delivered to EXUS, so that EXUS will be able to develop and train its AI-models. To ensure that there is not a possibility that the actual identity of the person or sensitive data related to this person could lead to its actual identification and since simple pseudonymization techniques as mentioned above are not sufficient, EPIBANK will deliver the data needed, after one of the ENCRYPT's privacy-preserving methods will be applied to the data. Since the privacy-preserving method selected will have a direct impact on the AI model's development and training phases as well as to the performance/accuracy of the models developed, all ENCRYPT's privacy-preserving methods have to be tested and validated so that the optimum solution will be opted.
To this extent, the CTI use case will tackle the above objectives and concerns. A CTI gathering, extraction and sharing tool will be used to collect, combine, and correlate data from various internal (i. e. , data from ENCRYPTs' end-users) and external sources, such as social media platforms and vulnerability databases. During the use case, different techniques will be used such as extraction of Indicators of Compromise (IoCs), correlation using the MISP (Malware Information Sharing Platform) correlation engine  [1]  (simple correlation), Exploratory Data Analysis (EDA) and Machine Learning analysis for advanced correlation (using internal and external data). Thanks to the CTI tool, personal information such is automatically rectified during data gathering. The CTI Extraction component minimizes and anonymizes the data, while the CTI correlation component pseudonymizes the data from ENCRYPT's data providers and external sources.
The CTI sharing component provides a secure platform for sharing information with interested parties while maintaining data owner privacy. However, regarding the previous techniques, there is still a risk of data privacy violation during the data gathering and CTI sharing processes. Nevertheless, implementing strong privacy-preserving techniques can address these concerns by securing the privacy of data holders. During the CTI use case scenario, data providers will use ENCRYPT's privacypreserving techniques to anonymize their data before sending it to the data processor for the extraction of CTI.
IV. LEGAL CONSIDERATIONS ENCRYPT delivers a privacy-preserving framework, which aims at incorporating an ethically and legally aware design. Data protection by design is a legal obligation -Art. 25 GDPR  [2] , which entails that organizations and entities implement technical and organizational measures to integrate data protection principles, such as data minimization and purpose limitation, in the manipulation and processing of personal data. In addition, security by design is a key component of the new proposed law, the Cyber Resilience Act (CRA). Both design obligations contribute to enhancing trust and security, allowing for the processing of data, while respecting privacy and data protection. The ENCRYPT user-centric framework allows data owners to comply with those obligations, by processing data, without sharing sensitive or other information to unauthorized parties.
ENCRYPT offers a scalable privacy solution by allowing the users to determine the purposes of the processing and their needs, which is in line with Art. 5(2) GDPR and the accountability principle. In addition, the ENCRYPT framework, and its recommendation engine will integrate by default legal and ethical risks, alongside risks of technical nature, allowing for a warning system providing the trade-offs between privacy and data exploitation, when envisaged uses of the data, are not meeting the thresholds and requirements imposed by the GDPR. V. FRAMEWORK ARCHITECTURE ENCRYPT proposes an intelligent and user-centric framework (Fig. 1 ) for the confidential processing of privacysensitive data via configurable, optimizable, and verifiable privacy-preserving techniques and its overall architecture is given in the figure below.
Computation, Differential Privacy, Trusted Execution Environment. ENCRYPT builds on top of these techniques making them configurable in terms of security and performance. It intelligently uses them by also combining their intrinsic security mechanisms to mitigate limitations and take advantage of their benefits. Since in most cases, performance represents the Achilles' heel of solutions for privacy-preserving computing, ENCRYPT provides a transparent to the user GPU-based acceleration service that is capable of being used in conjunction with each technology or cryptographic scheme that can exploit parallel processing of confidential data. The platform also encloses intelligent units to adjust itself to users' and data requirements. More specifically, it comes with an AI-based recommendation system, which provides data owners with recommendations on the privacy level necessitated for their data, and data processors with suggestions on the deployment and configuration of privacy-preserving technologies based on the type of data they want to process.
Taking into account the lack of familiarization with privacypreserving technologies, the ENCRYPT recommendation system will provide intelligible suggestions tailored to the needs of different types of users. ENCRYPT also provides data owners with a prototype that streamlines the continuous assessment of privacy risks for a given personal data processing. The knowledge graph building tool assists data owners and data stewards in standardising their datasets, so that their processing is performed in a GDPR-compliant way, based on the assessment of the ENCRYPT recommendation system. On the data processors side, a privacy quantification tool assists data processors in identifying the level of privacy offered by specific configurations of privacy-preserving technologies, as well as the level of privacy required for the data they want to process.
As a result, the recommendation system will match specific privacy requirements imposed by the types of data to be processed, with a specific deployment and configuration plan for the type of processing the data processor wants to perform. For the optimised deployment of privacy-preserving technologies, a data pre-processing module developed within ENCRYPT will process data and configure datasets in a way that facilitates the execution of the privacy-preserving technology selected. Automation of this process will ease the burden of configuration from the data owners or data stewards. For the HE case, this component will analyse the data/features to identify which contribute more to the objective of a given analysis and encrypt only those. This will allow users to experience less computational overhead, improving scalability.
Similarly, for the case of DP, a privacy budget optimiser will analyse data to identify the privacy protection -data utility curve for each specific dataset. This will allow data owners and stewards to make an informed decision on the level of noise added in their datasets to ensure adequate levels of privacy, while minimising detrimental effects to data utility. Similarly, on the data processor side, and in order to facilitate the deployment of privacy-preserving technologies, ENCRYPT will develop a lightweight data post-processing module in order to allow the easier recuperation and exploitation of the results of execution for the selected privacy-preserving technologies. In an indicative ENCRYPT user journey, the data owner or data steward provides the recommendation system with information about the data they have, with the support of the Knowledge Graph tool that supports interoperability between different data types.
The recommendation system responds with suggestions on the level of privacy needed, and technologies supporting this level of privacy. The service provider/data processor provides other requirements on the expected performance and functionality. This information is sent via dedicated ENCRYPT UIs to the AIbased recommendation system, which selects the most appropriate technology (or combination of technologies), as well as the configuration of privacy parameters. The Microservice orchestrator (the project will consider Kubernetes and OpenStack for the orchestration) -based on the inputs coming from the recommendation system -deploys the hosting machines with related microservices to support the selected ENCRYPT solutions, and to meet possible constraints posed by the data owner and data processor.
On the data owner side, the ENCRYPT data pre-processing service is executed to format and configure data appropriately for the selected privacypreserving technology to be used. When DP is selected as the privacy-preserving technology to be deployed for a given application, the privacy budget optimiser is responsible for configuring the noise levels, as calculated by the AI-based recommendation engine. Similarly, for SMPC, this service splits data in pieces to be stored in distributed servers, and in FHE it homomorphically encrypts only the necessary data. During runtime, the orchestrator monitors performance metrics, and spawns new services to ensure that the Service Level Agreements (SLAs) with regard to delay are met. The data postprocessing service, deployed on the side of the entities interested by the outputs of the selected privacy-preserving technologies (either researchers, service providers or data processors) helps them recuperate the results in an easily understandable and readable form (e.
In parallel with the investigation of the combination of the homomorphic encryption with TEE, one of the objectives of ENCRYPT project is to conceive and implement protocols for federated data processing based on homomorphic encryption with support for multi-users (e. g. threshold homomorphic encryption). Another step forward to the deployment of HE at a larger scale requires efficient ways of transciphering, i. e. combine homomorphic computation with exiting, lighter symmetric encryption. This will be another direction which will be investigated by ENCRYPT team, since this allows to improve the scalability of the overall system by diminishing the memory required for the encryption and the transmission of the private, sensitive data to the processing server. Another line of research consists in increasing the performances of non-linear homomorphic operators by using the functional bootstrapping, an interesting method offered by TFHE cryptosystem.
C. Differential Privacy Differential Privacy  [10]  has several properties that make it particularly useful in applications such as those envisioned by the ENCRYPT project: composability, group privacy, and robustness to auxiliary information. Composability enables modular design of mechanisms: if all the components of a mechanism are differentially private, then the same holds for their composition. Group privacy implies graceful degradation of privacy guarantees if datasets contain correlated inputs, such as the ones contributed by the same individual, while robustness to auxiliary information means that privacy guarantees are not affected by any side information available to the adversary. In principle, during the iterative method for optimizing the objective functions in AI systems, this methodology averages together multiple updates induced by training data examples, clips each of these updates, and adds some kind of noise (e.
g. , Gaussian) to the final average. Based on the above, ENCRYPT will offer the expansion of privacy-preserving libraries for Deep Learning with additional guarantees that can usefully strengthen the protections offered by other privacy techniques. ENCRYPT will offer to the user a tool for the optimization and tuning of the "privacy budget" parameter, which comes in differential privacy techniques to quantitatively dial up or down the privacy guarantee  [11]. Additionally, ENCRYPT will make good use of the "strength" that differential privacy is parametric: privacy does not come for free. Asking for more privacy will have the cost of either diminished data utility or the need to collect more data.
Assessment of minimal residual disease in acute lymphoblastic leukemia by immune repertoire NGS requires spiking CDR3 sequences at known quantities into the patient's sample. Recently, the EuroClonality-NGS group released one of the most comprehensive protocols for this purpose. ARResT/Interrogate is a closed-source software for processing these NGS libraries, developed by this same group. Vidjil, an open-source alternative, currently cannot handle libraries prepared using this protocol. Here, we present a Vidjil add-on to solve this issue. EuroClonality-NGS prepared samples analyzed with Vidjil and ARResT/Interrogate were highly concordant (r = 0. 998) and presented low error (root-mean-square error, RMSE = 0. 112).
Pearson's correlation coefficient represented by r, and root-mean-square error by RMSE. Vidjil analysis without (A) and with manual clonotype clustering (B). (C, D, and E) ARResT/Interrogate and Vidjil comparison per sample of total cIT-QC read count (C), total IG/TR rearrangement read count (D), and total cIT-QC read frequency (total cIT-QC reads/total V(D)J reads) (E). Green and black arrows indicate samples where a cIT-QC sequence was missing in both software tools and only in Vidjil, respectively. CDR3 region at predetermined copy numbers. These control clonotypes are then used to establish a cell/read ratio for the sample, which in turn is used to find the number of cells for leukemia clonotypes. In recent years, protocols for this purpose have been established by several groups, with one of the most comprehensive and prominent examples being released by the EuroClonality-NGS group  [4] [5] [6] [7] [8].
Analysis of immune repertoire NGS assays requires specialized software capable of grouping the resulting reads into clonotypes. Furthermore, compatibility with these spike-in-based normalization methods needs to be specifically built into the software in order to obtain a final result expressed in cell frequency. The EuroClonality-NGS method is integrated into ARResT/Interrogate, a closed-source platform developed by this same group  [9]. An open-source alternative for ARResT/Interrogate is Vidjil, which also provides a web environment for immune repertoire NGS analysis  [10, 11]. Being open-source, it allows one to either use the official Vidjil server or perform a local installation and customization. Vidjil, however, is not currently compatible with the EuroClonality-NGS normalization protocol.
This work, therefore, aimed to create an add-on to implement this method into the In order to validate this add-on, 32 NGS files from diagnosis (4 samples, 18 libraries) and aplastic posttreatment (4 samples, 14 libraries) B/T-ALL bone marrow, which were analyzed using ARResT/Interrogate in the cIT-QC original publication, were reanalyzed using Vidjil  [6]. The cell frequency for the reported clonotypes of interest (n = 62) was compared (Figure  1A ). These values, in log 10 scale, were highly concordant between both software tools (r = 0. 997) and presented low error (root-mean-square error, RMSE = 0. 115). The Vidjil web application allows the user to manually cluster clonotypes, which were not automatically clustered due to small sequencing errors. Applying this technique to clonotypes differing in only one nucleotide had a low, but positive impact on the concordance (r = 0.
In this context, Vidjil was able to identify all expected cIT-QC sequences in 25/32 samples (78. 1%), while ARResT/Interrogate did so in 29/32 samples (90. 6%). In the seven cases where Vidjil failed to identify all cIT-QC, no more than one sequence was missing. Importantly, while the total cIT-QC reads identified by both software tools was always very similar, Vidjil always identify slightly less cIT-QC sequences, which explains its tendency to generate marginally higher quantification values in comparison to ARResT/Interrogate (Figure  1C ). Total IG/TR read counts per sample were mostly very similar between both programs, with the exception of sample S13 where Vidjil was able to identify more than double the amount of reads in comparison to ARResT/Interrogate (Figure  1D ). The cIT-QC read frequencies (total cIT-QC reads/total V(D)J reads) reported by the two programs showed some discrepancy (Figure  1E ) in a few samples, but differences seem to be more associated with high cIT-QC frequencies rather than with missing sequences.