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.

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.

Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals’ private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.

The Geospatial and Information Substitution and Anonymization Tool (GISA) incorporates techniques for obfuscating identifiable information from point data or documents, while simultaneously maintaining chosen variables to enable future use and meaningful analysis. This approach promotes collaboration and data sharing while also reducing the risk of exposure to sensitive information. GISA can be used in a number of different ways, including the anonymization of point spatial data, batch replacement/removal of user-specified terms from file names and from within file content, and aid with the selection and redaction of images and terms based on recommendations using natural language processing. Version 1 of the tool, published here, has updated functionality and enhanced capabilities to the beta version published in 2023. Please see User Documentation for further information on capabilities, as well as a guide for how to download and use the tool. If there are any feedback you would like to provide for the tool, please reach out with your feedback to edxsupport@netl.doe.gov. Disclaimer: This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. The Geospatial and Information Substitution and Anonymization Tool (GISA) was developed jointly through the U.S. DOE Office of Fossil Energy and Carbon Management’s EDX4CCS Project, in part, from the Bipartisan Infrastructure Law.

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.

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.

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.

pone.0285212.t004 - A distributed computing model for big data anonymization in the networks

According to Cognitive Market Research, the global Data Masking Market size will be USD 18.43 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 18.51% from 2024 to 2031. Market Dynamics of Data Masking Market

Key Drivers for Data Masking Market

Increasing Data Breaches and Cybersecurity Threats- One of the main reasons for the Data Masking Market growth is the escalating frequency and sophistication of data breaches and cybersecurity threats that drive the demand for data masking solutions. By obfuscating sensitive information in non-production environments, data masking helps mitigate the risk of unauthorized access and data exposure, safeguarding organizations against potential security breaches and reputational damage.
The compliance requirements for data privacy and protection drive masking are anticipated to drive the Data Masking market’s expansion in the years ahead.

Key Restraints for Data Masking Market

The compliance complexities hinder data masking implementation in regulated industries.
The challenges in maintaining data usability while ensuring effective masking impact the market growth.

Introduction of the Data Masking Market

Data masking is the increasing emphasis on data privacy and regulatory compliance. With stringent data protection regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), organizations are under pressure to safeguard sensitive information from unauthorized access and disclosure. Data masking techniques enable organizations to anonymize or pseudonymize sensitive data while preserving its utility for testing, development, or analytics purposes. As the consequences of data breaches and non-compliance become more severe, businesses across industries are investing in data masking solutions to mitigate risks, maintain regulatory compliance, and protect their reputation, thus driving the growth of the data masking market.

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.

Purpose and Features

The purpose of the model and dataset is to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs. The model is a fine-tuned version of "Distilled BERT", a smaller and faster version of BERT. It was adapted for the task of token classification based on the largest to our knowledge open-source PII masking dataset, which we are releasing simultaneously. The model size is 62 million parameters. The… See the full description on the dataset page: https://huggingface.co/datasets/ai4privacy/pii-masking-43k.

Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals’ private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.

The French express high expectations in terms of information in the event of the development of other similar applications, primarily on the anonymization of data (84 percent) and the methods of control, in particular by the user himself (81 percent).

StopCovid is a project that is part of the state of health emergency linked to the coronavirus epidemic. This project would consist of a smartphone application intended to limit the spread of the virus by identifying the transmission chains through the collection of somewhat personal infomation of French app users. In general, French people were rather in favor of the app .

The objective of the NBR Pilot Project was to determine whether concealing personal information (NBR assessment method) which could lead to the identification of a candidate’s origin from job applications, had an impact on the screening decisions made by reviewers when compared to the Traditional assessment method where all personal information was presented.

The contactless biometrics technology market is experiencing robust growth, projected to reach $8.376 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 16.2% from 2025 to 2033. This expansion is driven by several key factors. Increasing security concerns across various sectors, from banking and finance to healthcare and government, are fueling demand for advanced authentication systems that offer enhanced security and hygiene compared to traditional methods. The rising adoption of smartphones and other smart devices equipped with biometric capabilities further accelerates market growth. Furthermore, the contactless nature of these technologies aligns perfectly with post-pandemic hygiene protocols, boosting their appeal in public spaces and commercial settings. Technological advancements, such as improved sensor accuracy and faster processing speeds, are also contributing to the market's expansion. While data privacy concerns remain a potential restraint, the industry is actively addressing these issues through robust data encryption and anonymization techniques. Market segmentation reveals strong growth across various applications, including government initiatives for border control and identity management, the banking sector's focus on fraud prevention, and the increasing integration of contactless biometrics into consumer electronics. The geographic distribution of the market shows significant contributions from North America and Europe, driven by early adoption and technological advancements. However, the Asia-Pacific region is expected to experience the fastest growth due to burgeoning economies, rising disposable incomes, and increasing government investments in infrastructure projects incorporating biometric technologies. Key players in the market are continuously innovating to enhance product features, expand their product portfolio, and forge strategic partnerships to strengthen their market position. The competitive landscape is dynamic, with both established players and emerging companies striving to capitalize on the market's potential. The long-term outlook for the contactless biometrics market remains positive, indicating continued substantial growth throughout the forecast period.

We collected a trace of wireless network activity at SIGCOMM 2008. The subjects of the traced network chose to participate by joining the traced SSID. The release contains 3 types of anonymized traces: 802.11a, Ethernet and Syslog from the Access Point. We anonymized the trace data using a modified version (http://www.cs.umd.edu/projects/wifidelity/sigcomm08_traces/sigcomm08-tcpmkpub.tar.gz) of the tcpmkpub tool (http://www.icir.org/enterprise-tracing/tcpmkpub.html) The packet traces include anonymized DHCP and DNS headers.last modified : 2009-03-25release date : 2009-03-02date/time of measurement start : 2008-08-17date/time of measurement end : 2008-08-21collection environment : We collected a trace of wireless network activity at SIGCOMM 2008. The subjects of the traced network chose to participate by joining the traced SSID. Our goal is to gather a detailed trace of network activity at SIGCOMM 2008 to improve 802.11 tracing techniques as part of the Wifidelity project and enable analysis of the behavior of a wireless LAN that is (presumably) heavily used.network configuration : We used four BSSIDs on four channels with one NAT (Network Address Translation) router. To collect the traces, we deployed eight 802.11a monitors so 2 monitors are assigned to each channel. A Xirrus Wi-Fi Array (http://www.xirrus.com/products/arrays-80211abg.php) provided the traced 802.11a network (SSID:SIGCOMM-ONLY-Traced). The WiFi Array consisted of four BSSIDs that were broadcast on four 802.11a channels. After anonymization, the DHCP assigned IP addresses for clients are in the following subnets: 26.12.0.0/16 and 26.2.0.0/16.data collection methodology : We recorded network protocol information from all wired and wireless packets sent on the wireless network of SSID:SIGCOMM-ONLY-Traced. Each packet includes physical layer information (in the Prism header) such as the wireless signal strength as well as the 802.11, IP, TCP, UDP, and ICMP headers, depending on the packet type. We did not record packet payloads above the transport layer except for DHCP and DNS payloads. However, we anonymized or deleted potentially sensitive information such as MAC and IP addresses, and DHCP and DNS headers.sanitization : The user chose to participate in the trace by associating with the SIGCOMM-ONLY-Traced SSID. Otherwise, the users joined the "Untraced" SSID: SIGCOMM-ONLY-Untraced. The traces do not contain any data from the "Untraced" SSID. We anonymized the traces to protect the identity and activity of users who opted to be traced during SIGCOMM 2008. - Filtering 802.11a traces Each packet in the wireless traces meets one or both of the following criteria: 1. BSSID address matches the "traced" BSSID. 2. Packet is a probe request for the "SIGCOMM-ONLY-Traced" SSID. - Filtering Ethernet traces The AP was set up with a monitor VLAN for the "SIGCOMM-ONLY-Traced" network. - Filtering Syslog traces The syslog trace only contains information about users associated with the "traced" network. The method to filter out syslog messages about "Untraced" users is as follows: Include all syslog messages while a client is associated to the "traced" network. The syslog messages indicate when a client associates to, and disassociates from the "traced" network.Tracesetumd/sigcomm2008/pcapPCAP traceset of wireless network measurement in SIGCOMM 2008 conference.file: sigcomm08_traces.tar.gzdescription: We collected pcap traces of wireless network activity at SIGCOMM 2008. The subjects of the traced network chose to participate by joining the traced SSID.measurement purpose: Network Diagnosismethodology: 1. 802.11a During most of the conference approximately two 802.11a monitors were placed at the four corners of the main conference hall. We did not record the exact location of each monitor. However, we tried to capture each channel with two monitors placed at opposite corners of the room. 2. Ethernet Packets sent from the NAT to the AP and from the AP to the NAT were captured using an Ethernet trace collector attached to the packet dump port on the WiFi Array.sanitization: The packets are anonymized using a modified version of the tcpmkpub tool. The tool is available from the download link of [sigcomm08-tcpmkpub.tar.gz]. Metadata about the trace anonymization is provided in the file tcpmkpub.log.export. In the description below, [new] indicates new functionality added to tcpmkpub, and [tcpmkpub] indicates the functionality of the original tcpmkpub tool, described in the following reference: R. Pang, M. Allman, V. Paxson, and J. Lee. The Devil and Packet Trace Anonymization SIGCOMM Computer Communication Review, 2006. [Crypto-PAn] indicates the functionality of the original tcpmkpub tool, described in the following reference: Xu, J. Fan, M. H. Ammar, and S. B. Moon. Prefix-preserving IP address anonymization: measurement-based security evaluation and a new cryptography-based scheme. In Proceedings of the IEEE International Conference on Network Protocols (ICNP), pages 280–289, Nov. 2002. 1. Checksums (IP/UDP/TCP) [tcpmkpub] The anonymization code recomputes checksums. The anonymization meta-data (tcpmkpub.log.export) holds information about packets in the traces with bad checksums. Bad checksums are indicated in the anonymized traces by a 1 in the checksum field, or 2 if the checksum was 1, A UDP checksum of 0 is not changed. 2. Link Layer A. Ethernet [tcpmkpub] MAC Addresses: - The 3 high and low-order bytes are hashed separately. - The high-order 3 bytes are hashed to retain vendor information. - Addresses containing all 1's or all 0's are not changed. - The Multicast bit is retained. B.VLAN [new] The vlan header did not need to be anonymized. C. 802.11 [new] - MAC addresses are anonymized using the same method as the Ethernet MAC addresses. - If the packet is fragmented (fragment bit == 1 or fragment # > 0), skip the rest of the packet. 3. Network Layer A. IP [tcpmkpub] - External addresses hashed using prefix preserving scheme [Crypto-PAn]. - Internal addresses hashed to unused prefix by the external addresses and the subnet and host portions of the address are transformed. - Multicast addresses are not anonymized. - The [tcpmkpub] paper recommends removing packets from network scanners. We did not determine this was a threat to our network as the identity tied to a local address was dynamic. B. ARP [tcpmkpub] - If the ARP packet contains a partial IP packet, use the IP anonymization above. - IP addresses anonymized using the IP anonymization procedure above. 4. Transport Layer A. TCP [tcpmkpub] - The TCP timestamp options are transformed into separate monotonically increasing counters with no relationship to time for each IP address in the anonymized trace. - If timestamp is 0 do not modify it. - Replace timestamp with a unique number incremented in the order of the trace. B. UDP [tcpmkpub] Recompute checksum according to checksum policy above. 5. Application Layer A. DNS [new] - Anonymize DNS labels individually by taking the Keyed-HMAC of the label. - Keep the low-order 8 bytes of the hash digest as the label. - Convert the digest to ASCII by converting to hex. - Store the new length of the DNS packet in the following fields: [IP/UDP/DNS,PCAP Captured, PCAP On Wire]. - Anonymize any type 'A' resource record data using the IP anonymization scheme above. DNS Packets may be cut off because of the snaplen at capture. B. DHCP [new] - Client IP address is anonymized. - Client hardware address is anonymized. - Your IP address (yiaddr) is anonymized. The rest of the DHCP packets were cut off by the snaplen at capture.umd/sigcomm2008/pcap Traces802.11a: PCAP traces of wireless network measurement collected from the wireless side in SIGCOMM 2008 conference.configuration: During most of the conference approximately two 802.11a monitors were placed at the four corners of the main conference hall. We did not record the exact location of each monitor. However, we tried to capture each channel with two monitors placed at opposite corners of the room. The network topology is configured as follows: Users: 26.12.*.* 26.2.*.* Network Management: 26.6.*.*format:sigcomm08_wl_(monitor #)_(first packet time)_(last packet time)_(bssid)_(channel).pcapEthernet: PCAP traces of wireless network measurement collected from the Ethernet side in the SIGCOMM 2008 conference.configuration: Packets sent from the NAT to the AP and from the AP to the NAT were captured using an Ethernet trace collector attached to the packet dump port on the WiFi Array. The network topology is configured as follows: Users: 26.12.*.* 26.2.*.* Network Management: 26.6.*.*format:sigcomm08_eth_(first packet time)_(last packet time).pcapanonymization_log: The anonymization log of wireless network traces in the SIGCOMM 2008 conference.configuration: tcpmkpub anonymization log for the traces 'umd/sigcomm2008/pcap/802.11a' and 'umd/sigcomm2008/pcap/Ethernet', and md5 checksums for the trace files.format:The anonymization log file name is 'tcpmkpub.log.export'.umd/sigcomm2008/syslogSyslog traceset of wireless network measurement in the SIGCOMM 2008 conference.file: sigcomm08_syslog.tar.gzdescription: We collected syslog traces of wireless network activity at SIGCOMM 2008. The subjects of the traced network chose to participate by joining the traced SSID.measurement purpose: Network Diagnosismethodology: A tracing box connected to the Array's management port collected syslog traces. Unfortunately, after the conference we noticed that these traces were corrupted. However, we were able to salvage one of the syslog traces because we collected it with the Ethernet tracing box.sanitization: macmkpub, a MAC address anonymizer based on the tcpmkpub anonymization code, anonymized the MAC addresses in the syslog traces. Metadata about the trace anonymization is provided in the file 'tcpmkpub.log.export'.umd/sigcomm2008/syslog TracesEthernet: Syslog traces of wireless network measurement in the SIGCOMM 2008

This file contains de-identified and anonymized healthcare facility-level raw primary data used in the analysis.

To maintain efficient myocardial function, optimal coordination between ventricular contraction and the arterial system is required. Exercise-based cardiac rehabilitation (CR) has been demonstrated to improve left ventricular (LV) function. This study aimed to investigate the impact of CR on ventricular-arterial coupling (VAC) and its components, as well as their associations with changes in LV function in patients with acute myocardial infarction (AMI) and preserved or mildly reduced ejection fraction (EF). Effective arterial elastance (EA) and index (EAI) were calculated from the stroke volume and brachial systolic blood pressure. Effective LV end-systolic elastance (ELV) and index (ELVI) were obtained using the single-beat method. The characteristic impedance (Zc) of the aortic root was calculated after Fourier transformation of both aortic pressure and flow waveforms. Pulse wave separation analysis was performed to obtain the reflection magnitude (RM). An exercise-based, outpatient cardiac rehabilitation (CR) program was administered for up to 6 months. Twenty-nine patients were studied. However, eight patients declined to participate in the CR program and were subsequently classified as the non-CR group. At baseline, E’ velocity showed significant associations with EAI (beta -0.393; P = 0.027) and VAC (beta -0.375; P = 0.037). There were also significant associations of LV global longitudinal strain (LV GLS) with EAI (beta 0.467; P = 0.011). Follow-up studies after a minimum of 6 months demonstrated a significant increase in E’ velocity (P = 0.035), improved EF (P = 0.010), and LV GLS (P = 0.001), and a decreased EAI (P = 0.025) only in the CR group. Changes in E’ velocity were significantly associated with changes in EAI (beta -0.424; P = 0.033). Increased aortic afterload and VA mismatch were associated with a negative impact on both LV diastolic and systolic function. The outpatient CR program effectively decreased aortic afterload and improved LV diastolic and systolic dysfunction in patients with AMI and preserved or mildly reduced EF.

BackgroundsCaregivers are essential in the care of a patient with digestive cancer. Considering their experience and needs is crucial.ObjectivesTo explore the experience of caregivers of patients with digestive cancer and to compare the perspectives of patients and caregivers.MethodsA mixed-methods study with a cross-sectional prospective and a comprehensive qualitative dimension was performed in a medical oncology unit in a French tertiary hospital. Dyads made of patients with digestive cancer and their caregiver were recruited. The Caregiver Reaction Assessment (CRA) and the Supportive Care Needs Survey for Partners and Caregivers (SCNS-PC) questionnaires were distributed to caregivers. The CRA was used to measure the caregiver burden and the SCNS-PC was used to identify the unmet supportive care needs of caregivers. Semi-structured interviews with the dyads were conducted. Qualitative interviews addressed various dimensions of the caregiver’s experience from each dyad’s member perspective.ResultsThirty-two caregivers completed the questionnaires. Responses showed high self-esteem, schedule burden, and a need for care and information services. Ten dyads participated in the interviews. Three themes emerged from the caregiver’s interviews: illness is an upheaval; loneliness and helplessness are experienced; caring is a natural role with positive outcomes. Four themes emerged from patient’s interviews: the caregiver naturally assumes the role and gets closer; he is the patient’s anchor; his life is disrupted; anxiety and guilt accompany the desire to protect him. In comparing patient and caregiver data, the main theme of disagreement was their relationship.ConclusionsCaregiver care does not appear to be optimal, particularly in terms of their need for information. Patients have a fairly good representation of their experience, but the caregivers’ opinion need to be considered.