The Bureau of Justice Statistics (BJS) Compendium of State Privacy and Security Legislation database consists of state laws, executive orders, and administrative regulations relating to the privacy and security of criminal history record information (CHRI). The Compendium database documents how the states and territories regulate the disclosure and security of CHRI. SEARCH (the National Consortium for Justice Information and Statistics) completed the previous version of the Compendium database for BJS in 2002. During 2014 and 2015, the Federal Research Division (FRD) of the Library of Congress updated the Compendium database for BJS.

Our research reports a systematic literature review of 49 publications on security studies with software developer participants. These attached files are: - A BibTeX file: includes all 49 references in BibTex format. - An Excel spreadsheet: our analysis of each publication. Each row represents a publication and columns represent features that we analysed such as number of participants, whether there was a clear research question, or whether the paper reports ethics. - Database queries: actual queries that we executed on databases.

Privacy-Preserving Feature Extraction for Detection of

Anomalous Financial Transactions

------------------------------------------------------------------------

This repository holds the code written by the PPMLHuskies for the 2nd Place solution in the PETs Prize Challenge, Track A.

Description

The task is to predict probabilities for anomalous transactions, from a

synthetic database of international transactions, and several synthetic

databases of banking account information. We provide two solutions. One

solution, our centralized approach, found in `solution_centralized.py`,

uses the transactions database (PNS) and the banking database with no

privacy protections. The second solution, which provides robust privacy

gurantees outlined in our report, follows a federated architecture,

found in `solution_federated.py` and model.py. In this approach, PNS

data resides in one client, banking data is divided up accross other

clients, and an aggregator handles all the communication between any

clients. We have built in privacy protections so that clients and the

aggregator learn minimal information about each other, while engaging in

communication to detect anomalous transactions in PNS.

The way in which we conduct training and inference in both the

centralized and the federated architectures is fundamentally the same

(other than the privacy protections in the latter). Several new features

are engineered from the given PNS data. Then a model is trained on those

features from PNS. Next, during inference, a check is made to determine

if attributes from a PNS transaction match with the banking data, or if

the associated account in the banking data is flagged. If any of these

attributes are amiss, we give it a value of 1, and a 0 otherwise.

Lastly, we take the maximum of the inferred probabilities from the PNS

model, and the result from the Banking data validation, which is used as

our final prediction for the probability that the transaction is

anomalous.

The difference between the federated and centralized logic is that in

the federated set up, where there are one or multiple partitions of the

banking data across clients, is that the PNS client engages in a

cryptographic protocol based on homomorphic encryption with the banking

clients, routed through the aggregator, to perform feature extraction.

This protocol, to ensure privacy, and that PNS does not learn anything

from the banks beyond the set membership of a select few features, is

carried out over several rounds, r. r = 7 + n, where n is the number of

bank clients.

Salient Features of Dentists Email Addresses

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2. 85% telephone and email verified Dentist Mailing Lists
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4. 15 million verification messages sent every month to validate email addresses
5. Connect with top Dentists across the US, Canada, UK, Europe, EMEA, Australia, APAC and many more countries.
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Data Sources

The contact details of your targeted healthcare professionals are compiled from highly credible resources like: • Websites • Medical seminars • Medical records • Trade shows • Medical conferences

What’s in for you? Over choosing us, here are a few advantages we authenticate- • Locate, target, and prospect leads from 170+ countries • Design and execute ABM and multi-channel campaigns • Seamless and smooth pre-and post-sale customer service • Connect with old leads and build a fruitful customer relationship • Analyze the market for product development and sales campaigns • Boost sales and ROI with increased customer acquisition and retention

Our security compliance

We use of globally recognized data laws like –

GDPR, CCPA, ACMA, EDPS, CAN-SPAM and ANTI CAN-SPAM to ensure the privacy and security of our database. We engage certified auditors to validate our security and privacy by providing us with certificates to represent our security compliance.

Our USPs- what makes us your ideal choice?

At DataCaptive™, we strive consistently to improve our services and cater to the needs of businesses around the world while keeping up with industry trends.

• Elaborate data mining from credible sources • 7-tier verification, including manual quality check • Strict adherence to global and local data policies • Guaranteed 95% accuracy or cash-back • Free sample database available on request

Guaranteed benefits of our Dentists email database!

85% email deliverability and 95% accuracy on other data fields

We understand the importance of data accuracy and employ every avenue to keep our database fresh and updated. We execute a multi-step QC process backed by our Patented AI and Machine learning tools to prevent anomalies in consistency and data precision. This cycle repeats every 45 days. Although maintaining 100% accuracy is quite impractical, since data such as email, physical addresses, and phone numbers are subjected to change, we guarantee 85% email deliverability and 95% accuracy on other data points.

100% replacement in case of hard bounces

Every data point is meticulously verified and then re-verified to ensure you get the best. Data Accuracy is paramount in successfully penetrating a new market or working within a familiar one. We are committed to precision. However, in an unlikely event where hard bounces or inaccuracies exceed the guaranteed percentage, we offer replacement with immediate effect. If need be, we even offer credits and/or refunds for inaccurate contacts.

Other promised benefits

• Contacts are for the perpetual usage • The database comprises consent-based opt-in contacts only • The list is free of duplicate contacts and generic emails • Round-the-clock customer service assistance • 360-degree database solutions

Presentation for the IDCC 2017 Conference in Edinburgh. This paper describes the Qualitative Data Repository’s configuration of a private cloud to manage sensitive data. In making the transition from a centralized host, QDR has relied on two evaluative frameworks – a questionnaire produced by the Cloud Security Alliance Consensus Assessments Initiative, and the NIST Framework for Cloud Usability.

GDPR-Masked Whois Database, discover comprehensive ownership details, registration dates, and more for domains registered in GDPR-Masked with Whois Data Center.

The Relational Database Software Market size was estimated at USD 21.97 Billion in 2024 and is projected to reach USD 45.23 Billion by 2031, growing at a CAGR of 9.4 % from 2024 to 2031

Global Relational Database Software Market Drivers

Rising Demand for Efficient Data Management: Organizations across industries are generating and collecting ever-increasing volumes of data. This necessitates efficient and secure data management solutions. Relational databases, with their structured format and robust querying capabilities, offer a valuable tool to organize, manage, and analyze this data, leading to increased demand for this software.

Cloud Adoption and Scalability: The proliferation of cloud computing has significantly impacted the relational database market. Cloud-based database solutions offer scalability, flexibility, and reduced IT infrastructure burden for businesses. This makes them particularly attractive for small and medium-sized enterprises (SMEs) and facilitates easier data access for geographically dispersed teams.

Growing Importance of Data Security and Compliance: Data breaches and cyberattacks pose significant threats to businesses. Relational database software vendors are constantly innovating to enhance security features like encryption and access controls. Additionally, stringent data privacy regulations like GDPR and CCPA are driving the need for compliant data storage and management solutions, further propelling the market for secure relational databases.

In 2023, the number of data compromises in the United States stood at 3,205 cases. Meanwhile, over 353 million individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2022, healthcare, financial services, and manufacturing were the three industry sectors that recorded most data breaches. The number of healthcare data breaches in the United States has gradually increased within the past few years. In the financial sector, data compromises increased almost twice between 2020 and 2022, while manufacturing saw an increase of more than three times in data compromise incidents. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

BackgroundHealthcare is facing a growing threat of cyberattacks. Myriad data sources illustrate the same trends that healthcare is one of the industries with the highest risk of cyber infiltration and is seeing a surge in security incidents within just a few years. The circumstances thus begged the question: are US hospitals prepared for the risks that accompany clinical medicine in cyberspace?ObjectiveThe study aimed to identify the major topics and concerns present in today's hospital cybersecurity field, intended for non-cyber professionals working in hospital settings.MethodsVia structured literature searches of the National Institutes of Health's PubMed and Tel Aviv University's DaTa databases, 35 journal articles were identified to form the core of the study. Databases were chosen for accessibility and academic rigor. Eighty-seven additional sources were examined to supplement the findings.ResultsThe review revealed a basic landscape of hospital cybersecurity, including primary reasons hospitals are frequent targets, top attack methods, and consequences hospitals face following attacks. Cyber technologies common in healthcare and their risks were examined, including medical devices, telemedicine software, and electronic data. By infiltrating any of these components of clinical care, attackers can access mounds of information and manipulate, steal, ransom, or otherwise compromise the records, or can use the access to catapult themselves to deeper parts of a hospital's network. Issues that can increase healthcare cyber risks, like interoperability and constant accessibility, were also identified. Finally, strategies that hospitals tend to employ to combat these risks, including technical, financial, and regulatory, were explored and found to be weak. There exist serious vulnerabilities within hospitals' technologies that many hospitals presently fail to address. The COVID-19 pandemic was used to further illustrate this issue.ConclusionsComparison of the risks, strategies, and gaps revealed that many US hospitals are unprepared for cyberattacks. Efforts are largely misdirected, with external—often governmental—efforts negligible. Policy changes, e.g., training employees in cyber protocols, adding advanced technical protections, and collaborating with several experts, are necessary. Overall, hospitals must recognize that, in cyber incidents, the real victims are the patients. They are at risk physically and digitally when medical devices or treatments are compromised.

The Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID) are a set of hospital databases that contain the universe of hospital inpatient discharge abstracts from data organizations in participating States. The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SID are based on data from short term, acute care, nonfederal hospitals. Some States include discharges from specialty facilities, such as acute psychiatric hospitals. The SID include all patients, regardless of payer and contain clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels. The SID contain clinical and resource-use information that is included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, admission and discharge status, patient demographics (e.g., sex, age), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. In addition to the core set of uniform data elements common to all SID, some include State-specific data elements. The SID exclude data elements that could directly or indirectly identify individuals. For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and county-level data from the Bureau of Health Professions' Area Resource File except in States that do not allow the release of hospital identifiers. Restricted access data files are available with a data use agreement and brief online security training.

The database security consulting service market is projected to reach a market size of XXX million by 2033, growing at a CAGR of XX% from 2025 to 2033. The increasing adoption of cloud computing, the growing number of cyberattacks, and the stringent data privacy regulations are driving the growth of this market. The database security consulting service market is segmented by application, type, and region. By application, the market is segmented into finance, retail, telecommunications, medical, manufacturing, and others. By type, the market is segmented into local database consulting and cloud database consulting. By region, the market is segmented into North America, South America, Europe, the Middle East & Africa, and Asia Pacific. The North American region is expected to dominate the market throughout the forecast period.

The global Key-Value Database market, valued at $1288.8 million in 2025, is poised for significant growth. Driven by the increasing adoption of cloud computing, the rise of big data analytics, and the expanding need for high-performance, scalable data storage solutions across various industries, the market is expected to experience substantial expansion throughout the forecast period (2025-2033). The cloud-based segment currently dominates the market, fueled by its inherent flexibility, scalability, and cost-effectiveness. Large enterprises are the primary consumers, leveraging key-value databases for applications demanding rapid data retrieval and high throughput, such as real-time analytics, caching, and session management. However, the on-premises segment continues to hold relevance, particularly among organizations with stringent data security and compliance requirements. Growth is further propelled by the increasing integration of key-value databases with other technologies such as NoSQL databases and distributed systems, offering enhanced functionality and interoperability. Geographic expansion, especially within developing economies of Asia Pacific and Middle East & Africa, will contribute significantly to market expansion. Competitive intensity is high, with established players like AWS, Azure, and others vying for market share alongside specialized providers focusing on specific niches. The market is further segmented based on application, with SMEs showing increasing adoption, driven by affordability and ease of implementation of cloud-based solutions. Despite the robust growth projections, the market faces certain challenges. Concerns around data security and privacy, especially within sensitive industries, coupled with the complexity of managing and scaling key-value databases can impede wider adoption. Furthermore, the need for specialized skills to effectively operate and maintain these systems can be a barrier to entry for smaller organizations. However, the ongoing development of user-friendly management tools and improved data security protocols will likely mitigate these concerns, fostering further market penetration. Overall, the Key-Value Database market is expected to remain a dynamic and competitive landscape with significant growth potential over the next decade.

This database is part of the National Medical Information System (NMIS). The National Health Care Practitioner Database (NHCPD) supports Veterans Health Administration Privacy Act requirements by segregating personal information about health care practitioners such as name and social security number from patient information recorded in the National Patient Care Database for Ambulatory Care Reporting and Primary Care Management Module.

Concerns about genetic privacy affect individuals’ willingness to accept genetic testing in clinical care and to participate in genomics research. To learn what is already known about these views, we conducted a systematic review, which ultimately analyzed 53 studies involving the perspectives of 47,974 participants on real or hypothetical privacy issues related to human genetic data. Bibliographic databases included MEDLINE, Web of Knowledge, and Sociological Abstracts. Three investigators independently screened studies against predetermined criteria and assessed risk of bias. The picture of genetic privacy that emerges from this systematic literature review is complex and riddled with gaps. When asked specifically “are you worried about genetic privacy,” the general public, patients, and professionals frequently said yes. In many cases, however, that question was posed poorly or only in the most general terms. While many participants expressed concern that genomic and medical information would be revealed to others, respondents frequently seemed to conflate privacy, confidentiality, control, and security. People varied widely in how much control they wanted over the use of data. They were more concerned about use by employers, insurers, and the government than they were about researchers and commercial entities. In addition, people are often willing to give up some privacy to obtain other goods. Importantly, little attention was paid to understanding the factors–sociocultural, relational, and media—that influence people’s opinions and decisions. Future investigations should explore in greater depth which concerns about genetic privacy are most salient to people and the social forces and contexts that influence those perceptions. It is also critical to identify the social practices that will make the collection and use of these data more trustworthy for participants as well as to identify the circumstances that lead people to set aside worries and decide to participate in research.

Elevate your marketing and sales strategies with our Global Email Address Data, providing unmatched access to a vast collection of email addresses, phone numbers, and comprehensive B2B and B2C contact information. Our data solutions empower businesses to enrich their outreach efforts, enabling effective online marketing and competitive intelligence.

Designed to enhance your data-driven strategies, our offerings include critical insights such as email address data, phone number data, B2B contact data, and B2C contact data. With our extensive resources, you can build strong connections and effectively engage your target audiences.

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The Healthcare Cost and Utilization Project (HCUP) Nationwide Emergency Department Sample (NEDS) is the largest all-payer emergency department (ED) database in the United States. yielding national estimates of hospital-owned ED visits. Unweighted, it contains data from over 30 million ED visits each year. Weighted, it estimates roughly 145 million ED visits nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels.

Sampled from the HCUP State Inpatient Databases (SID) and State Emergency Department Databases (SEDD), the HCUP NEDS can be used to create national and regional estimates of ED care. The SID contain information on patients initially seen in the ED and subsequently admitted to the same hospital. The SEDD capture information on ED visits that do not result in an admission (i.e., treat-and-release visits and transfers to another hospital). Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels.

The NEDS contain information about geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, including injuries). The NEDS contains clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). It includes ED charge information for over 85% of patients, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.Restricted access data files are available with a data use agreement and brief online security training.

The Healthcare Cost and Utilization Project (HCUP) State Emergency Department Databases (SEDD) contain the universe of emergency department visits in participating States. The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SEDD consist of data from hospital-based emergency department visits that do not result in an admission. The SEDD include all patients, regardless of the expected payer including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels. The SEDD contain clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and facilities (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, admission and discharge status, patient demographics (e.g., sex, age, race), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. In addition to the core set of uniform data elements common to all SEDD, some include State-specific data elements. The SEDD exclude data elements that could directly or indirectly identify individuals. For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and the Bureau of Health Professions' Area Resource File except in States that do not allow the release of hospital identifiers. Restricted access data files are available with a data use agreement and brief online security training.

Amazon AWS - Cloud Platforms & Services

Companies using Amazon AWS

We have data on 1,070,574 companies that use Amazon AWS. The companies using Amazon AWS are most often found in United States and in the Computer Software industry. Amazon AWS is most often used by companies with 10-50 employees and 1M-10M dollars in revenue. Our data for Amazon AWS usage goes back as far as 2 years and 1 months.

What is Amazon AWS?

Amazon Web Services (AWS) is a collection of remote computing services, also called web services that make up a cloud computing platform offered by Amazon.com.

Top Industries that use Amazon AWS

Looking at Amazon AWS customers by industry, we find that Computer Software (6%) is the largest segment.

Distribution of companies using Amazon AWS by Industry

 Computer software - 67, 537 companies  Hospitals & Healthcare - 54, 293 companies  Retail - 39, 543 companies  Information Technology and Services - 35, 382 companies  Real Estate - 31, 676 companies  Restaurants - 30, 302 companies  Construction - 29, 207 companies  Automotive - 28, 469 companies  Financial Services - 23, 680 companies  Education Management - 21, 548 companies

Top Countries that use Amazon AWS

49% of Amazon AWS customers are in United States and 7% are in United Kingdom.

Distribution of companies using Amazon AWS by country

 United Sates – 616 2275 companies  United Kingdom – 68 219 companies  Australia – 44 601 companies  Canada – 42 770 companies  Germany – 31 541 companies  India – 30 949 companies  Netherlands – 19 543 companies  Brazil – 17 165 companies  Italy – 14 876 companies  Spain – 14 675 companies

Contact Information of Fields Include:-

• Company Name • Business contact number • Title
• Name • Email Address • Country, State, City, Zip Code • Phone, Mobile and Fax • Website • Industry • SIC & NAICS Code • Employees Size
• Revenue Size
• And more…

Why Buy AWS Users List from DataCaptive?

• More than 1,070,574 companies
• Responsive database • Customizable as per your requirements • Email and Tele-verified list • Team of 100+ market researchers • Authentic data sources

What’s in for you?

Over choosing us, here are a few advantages we authenticate-

• Locate, target, and prospect leads from 170+ countries • Design and execute ABM and multi-channel campaigns • Seamless and smooth pre-and post-sale customer service • Connect with old leads and build a fruitful customer relationship • Analyze the market for product development and sales campaigns • Boost sales and ROI with increased customer acquisition and retention

Our security compliance

We use of globally recognized data laws like –

GDPR, CCPA, ACMA, EDPS, CAN-SPAM and ANTI CAN-SPAM to ensure the privacy and security of our database. We engage certified auditors to validate our security and privacy by providing us with certificates to represent our security compliance.

Our USPs- what makes us your ideal choice?

At DataCaptive™, we strive consistently to improve our services and cater to the needs of businesses around the world while keeping up with industry trends.

• Elaborate data mining from credible sources • 7-tier verification, including manual quality check • Strict adherence to global and local data policies • Guaranteed 95% accuracy or cash-back • Free sample database available on request

Guaranteed benefits of our Amazon AWS users email database!

85% email deliverability and 95% accuracy on other data fields

We understand the importance of data accuracy and employ every avenue to keep our database fresh and updated. We execute a multi-step QC process backed by our Patented AI and Machine learning tools to prevent anomalies in consistency and data precision. This cycle repeats every 45 days. Although maintaining 100% accuracy is quite impractical, since data such as email, physical addresses, and phone numbers are subjected to change, we guarantee 85% email deliverability and 95% accuracy on other data points.

100% replacement in case of hard bounces

Every data point is meticulously verified and then re-verified to ensure you get the best. Data Accuracy is paramount in successfully penetrating a new market or working within a familiar one. We are committed to precision. However, in an unlikely event where hard bounces or inaccuracies exceed the guaranteed percentage, we offer replacement with immediate effect. If need be, we even offer credits and/or refunds for inaccurate contacts.

Other promised benefits

• Contacts are for the perpetual usage • The database comprises consent-based opt-in contacts only • The list is free of duplicate contacts and generic emails • Round-the-clock customer service assistance • 360-degree database solutions

The global customer database software solutions market size was valued at USD XXX million in 2025 and is projected to expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2033. The growing need for effective customer relationship management (CRM) solutions and the increasing adoption of cloud-based CRM systems are key drivers of market growth. Furthermore, the increasing emphasis on data privacy and security is driving the demand for customer database software that offers robust security features. The market is segmented based on deployment type (cloud and on-premise), application (large enterprises, medium-sized businesses, and small businesses), and region (North America, South America, Europe, Middle East & Africa, and Asia Pacific). The cloud segment is expected to dominate the market due to its scalability, flexibility, and cost-effectiveness. North America is the largest regional market due to the presence of a large number of technology vendors and early adoption of CRM solutions. However, the Asia Pacific region is expected to witness significant growth due to the increasing demand for CRM solutions from emerging economies such as China and India.

The global Database as a Service (DBaaS) platform market is estimated to be valued at [USD 34.5 billion] in 2025 and is projected to reach [USD 112.6 billion] by 2033, exhibiting a [CAGR] of 14.9% during the forecast period (2025-2033). This growth can be attributed to the increasing adoption of cloud-based services, the growing need for data storage and management, and the rising popularity of big data analytics. The key drivers of the DBaaS platform market include the increasing adoption of cloud-based services by enterprises, the growing need for data storage and management, the rising popularity of big data analytics, and the increasing demand for data security and compliance. The major trends in the DBaaS platform market include the emergence of NoSQL databases, the adoption of artificial intelligence (AI) and machine learning (ML) in DBaaS platforms, and the growing popularity of multi-cloud and hybrid cloud deployments. The key restraints in the DBaaS platform market include the high cost of implementation, the security and privacy concerns associated with cloud-based services, and the lack of skilled professionals. The major segments in the DBaaS platform market include type (private cloud, public cloud, hybrid cloud), application (large enterprises, SMEs), and region (North America, Europe, Asia Pacific, Middle East & Africa, South America). The major companies in the DBaaS platform market include AWS, Caspio, DataStax, Fusioo, Google, IBM, Kintone, Microsoft, MongoDB, Ninox, Oracle, SAP, Zoho Corporation, and others.