The Data De-identification and Pseudonymization Software market is experiencing robust growth, projected to reach $1941.6 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 7.3%. This expansion is driven by increasing regulatory compliance needs (like GDPR and CCPA), heightened concerns regarding data privacy and security breaches, and the burgeoning adoption of cloud-based solutions. The market is segmented by deployment (cloud-based and on-premises) and application (large enterprises and SMEs). Cloud-based solutions are gaining significant traction due to their scalability, cost-effectiveness, and ease of implementation, while large enterprises dominate the application segment due to their greater need for robust data protection strategies and larger budgets. Key market players include established tech giants like IBM and Informatica, alongside specialized providers such as Very Good Security and Anonomatic, indicating a dynamic competitive landscape with both established and emerging players vying for market share. Geographic expansion is also a key driver, with North America currently holding a significant market share, followed by Europe and Asia Pacific. The forecast period (2025-2033) anticipates continued growth fueled by advancements in artificial intelligence and machine learning for enhanced de-identification techniques, and the increasing demand for data anonymization across various sectors like healthcare, finance, and government. The restraining factors, while present, are not expected to significantly hinder the market’s overall growth trajectory. These limitations might include the complexity of implementing robust de-identification solutions, the potential for re-identification risks despite advanced techniques, and the ongoing evolution of privacy regulations necessitating continuous adaptation of software capabilities. However, ongoing innovation and technological advancements are anticipated to mitigate these challenges. The continuous development of more sophisticated algorithms and solutions addresses re-identification vulnerabilities, while proactive industry collaboration and regulatory guidance aim to streamline implementation processes, ultimately fostering continued market expansion. The increasing adoption of data anonymization across diverse sectors, coupled with the expanding global digital landscape and related data protection needs, suggests a positive outlook for sustained market growth throughout the forecast period.

The Data Masking Software market is experiencing robust growth, driven by increasing regulations around data privacy (like GDPR and CCPA), the expanding adoption of cloud computing, and the surging need for secure data sharing across organizations. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% during the forecast period (2025-2033). This significant growth is fueled by several key factors, including the rising demand for data anonymization and pseudonymization techniques across various sectors like banking, healthcare, and retail. Companies are increasingly investing in data masking solutions to protect sensitive customer information during testing, development, and collaboration, thus mitigating the risk of data breaches and regulatory penalties. The diverse application segments, including Banking, Financial Services, and Insurance (BFSI), Healthcare and Life Sciences, and Retail and Ecommerce, contribute significantly to market expansion. Furthermore, the shift towards cloud-based solutions offers scalability and cost-effectiveness, further accelerating market adoption. The market segmentation reveals a strong preference for cloud-based solutions, driven by their inherent flexibility and ease of deployment. Within the application segments, the BFSI sector is currently leading due to stringent regulatory compliance needs and the large volume of sensitive customer data handled. However, growth in the healthcare and life sciences sector is expected to accelerate significantly as more institutions embrace digital transformation and the handling of patient data becomes increasingly regulated. Geographic growth is robust across North America and Europe, with Asia-Pacific showing significant potential for future expansion due to growing digitalization and increasing awareness of data security issues. While the market faces certain restraints such as the complexity of implementing data masking solutions and the high initial investment costs, the long-term benefits of robust data protection and compliance outweigh these challenges, driving consistent market expansion.

The cloud data desensitization market is experiencing robust growth, driven by increasing regulatory compliance needs (like GDPR and CCPA), the rising volume of sensitive data stored in the cloud, and the expanding adoption of cloud computing across diverse sectors. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. Key growth drivers include the escalating need to protect sensitive data from breaches and unauthorized access, particularly within healthcare (medical research data), finance (financial risk assessment), and government (government statistics). The cloud-based delivery model offers scalability and cost-effectiveness, further fueling market expansion. While strong security measures are integral to the success of this technology, challenges remain regarding the balance between data usability and robust security protocols. Integration complexities with existing infrastructure and the potential for unforeseen vulnerabilities represent key restraints. Market segmentation reveals a strong preference for cloud-based solutions, given their inherent flexibility and scalability. The application segments, medical research data, financial risk assessment, and government statistics, are currently leading the market, primarily due to the highly sensitive nature of the data involved. Leading vendors like Micro Focus, IBM, Thales, Google Cloud, and others are actively shaping the market landscape through continuous innovation and the introduction of advanced data masking and tokenization techniques. Regional analysis indicates strong growth in North America and Europe, driven by stringent data privacy regulations and a high concentration of organizations handling sensitive data. However, increasing adoption in the Asia-Pacific region, fueled by rapid digital transformation, is expected to significantly boost market growth in the coming years. The forecast period of 2025-2033 presents a significant opportunity for market expansion, driven by increased data security awareness and evolving technological advancements.

BackgroundAnonymization opens up innovative ways of using secondary data without the requirements of the GDPR, as anonymized data does not affect anymore the privacy of data subjects. Anonymization requires data alteration, and this project aims to compare the ability of such privacy protection methods to maintain reliability and utility of scientific data for secondary research purposes.MethodsThe French data protection authority (CNIL) defines anonymization as a processing activity that consists of using methods to make impossible any identification of people by any means in an irreversible manner. To answer project’s objective, a series of analyses were performed on a cohort, and reproduced on four sets of anonymized data for comparison. Four assessment levels were used to evaluate impact of anonymization: level 1 referred to the replication of statistical outputs, level 2 referred to accuracy of statistical results, level 3 assessed data alteration (using Hellinger distances) and level 4 assessed privacy risks (using WP29 criteria).Results87 items were produced on the raw cohort data and then reproduced on each of the four anonymized data. The overall level 1 replication score ranged from 67% to 100% depending on the anonymization solution. The most difficult analyses to replicate were regression models (sub-score ranging from 78% to 100%) and survival analysis (sub-score ranging from 0% to 100. The overall level 2 accuracy score ranged from 22% to 79% depending on the anonymization solution. For level 3, three methods had some variables with different probability distributions (Hellinger distance = 1). For level 4, all methods had reduced the privacy risk of singling out, with relative risk reductions ranging from 41% to 65%.ConclusionNone of the anonymization methods reproduced all outputs and results. A trade-off has to be find between context risk and the usefulness of data to answer the research question.

For the purpose of research on data intermediaries and data anonymisation, it is necessary to test these processes with realistic video data containing personal data. For this purpose, the Treumoda project, funded by the German Federal Ministry of Education and Research (BMBF), has created a dataset of different traffic scenes containing identifiable persons.

This video data was collected at the Autonomous Driving Test Area Baden-Württemberg. On the one hand, it should be possible to recognise people in traffic, including their line of sight. On the other hand, it should be usable for the demonstration and evaluation of anonymisation techniques.

The legal basis for the publication of this data set the consent given by the participants as documented in the file Consent.pdf (all purposes) in accordance with Art. 6 1 (a) and Art. 9 2 (a) GDPR. Any further processing is subject to the GDPR.

We make this dataset available for non-commercial purposes such as teaching, research and scientific communication. Please note that this licence is limited by the provisions of the GDPR. Anyone downloading this data will become an independent controller of the data. This data has been collected with the consent of the identifiable individuals depicted.

Any consensual use must take into account the purposes mentioned in the uploaded consent forms and in the privacy terms and conditions provided to the participants (see Consent.pdf). All participants consented to all three purposes, and no consent was withdrawn at the time of publication. KIT is unable to provide you with contact details for any of the participants, as we have removed all links to personal data other than that contained in the published images.

Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES) and this corresponding dataset aim to provide tools for measuring user enjoyment from an external perspective to supplement self-reported user enjoyment responses in human-robot interaction research, with future potential application for autonomous detection of user enjoyment in real-time in robots and agents for adapting conversations contingently to provide enjoyable and long-lasting interactions.

The dataset consists of 25 older adults' (12 men, 13 women) open-domain dialogue with an autonomous companion robot with an integrated large language model (GPT-3.5, text-davinci-003) from participatory design workshops conducted in March 2023. The conversations are annotated for user enjoyment based on HRI CUES by 3 expert annotators, as described in the paper (arXiv:2405.01354). Robot architecture and participatory design workshops are described in DOI: 10.21203/rs.3.rs-2884789/v1.

Exchanges file contains the participant ID, the number of the turn (conversation exchange by Robot-Participant response), the start and end of the turn, the anonymized transcript for the turn, and three annotator scores for the user enjoyment in the exchange.

Overall file contains the participant ID, self-reported user perception scores from the questionnaire ("I was satisfied with my conversation with the robot", "It was fun talking to the robot", "The conversation with the robot was interesting", "It felt strange talking to the robot") and three annotator scores for the user enjoyment in the overall interaction.

The conversations are in Swedish. Participants' mean age is 74.6 (SD=5.8). 20 participants had no prior interaction with a robot, and only one had previously talked with a robot. The average interaction duration is 7.4 min (SD=1.5) with 12 to 29 turns. Each turn lasts 5 to 61 seconds (M=17.7, SD=7.2). The total duration of the interactions is 174 min, corresponding to 590 turns.

Videos of the interactions are available upon request, contingent upon a signed agreement to maintain data confidentiality in accordance with GDPR regulations.

Anonymization macros:

[P_NAME]: Participant's name (may include surname). The robot always uses the first name even when the surname is given.

[NAME_REMOVED]: A name of another person mentioned by the participant.

[LOCATION_REMOVED]: Small town/village/area where the participant lives or lived.

[MEDICAL_INFO_REMOVED]: Medical information shared by the participant.

[AGE_REMOVED]: Participant's or other person's age.

[INFORMATION_REMOVED]: Sensitive information shared by the participant.

[MISTAKEN_NAME]: Speech recognition error resulted in the name being misunderstood.

This dataset consists of WiFi management frames (probe-requests, probe-responses, and beacons) captured during a passive in-field measurement campaign using the MITIK-SENS tool version 1 at La Rochelle Univeristy. Data collection was performed by five supersniffers (each with five sniffers) operating on WiFi channel 1 (2.4 GHz) across two scenarios, with four 60-minute experiments conducted. Anonymization of MAC addresses and SSIDs ensures GDPR compliance. Captured frames are stored as .pcap files. MITIK-SENS, 1.0 MITIK-MGMT, 1.0 The MITIK-LRU-V2 datasets can be utilized by the following tools in a structured workflow: MITIK-LINK is executed first to perform the MAC association of randomized MAC addresses used by the same device. It models the frame association to resolve MAC conflicts over small intervals. The output of this tool can then be used as input for the next step. More details: "https://gitlab.inria.fr/mitik/mac-association/mitik-link." MITIK-TRAJ takes the output of MITIK-LINK to reconstruct a mobile terminal’s trajectory by introducing the concept of bounded trajectories. It utilizes the signal strength of WiFi probe-requests, collected from measurements obtained via multiple sniffers. More details: "https://gitlab.inria.fr/mitik/trajectory-reconstruction/mitik-traj."

This dataset reports responses to a survey designed for investigating the relationship that humans have with their smart devices. The dataset has been collected in May-July 2020 and is a sample of over 500 respondents of various different ethnicities and backgrounds. These data have been used for modelling the ways people relate to their devices using the notion of agency. However, the data can be used for complementing any study that intends to investigate a tool-mediated communication from the perspective of the users and via a variety of attitudes and expectations the users invest in their devices and in themselves as users. This article presents the survey items as well as some raw data insights. The data have been collected in English and answers have been anonymized in order to ensure GDPR compliance. They are stored in a .csv file containing the respondents’ answers to the questions. The reference contact for this data at Tilburg University is Francesco Lelli The paper "A Dataset for Studying How Human Relates to their Smart Devices" provide an extensive description of the data as well as the methodology for collecting the samples.