Version 3 with 517M hashes and counts of password usage ordered by most to least prevalent Pwned Passwords are 517,238,891 real world passwords previously exposed in data breaches. This exposure makes them unsuitable for ongoing use as they re at much greater risk of being used to take over other accounts. They re searchable online below as well as being downloadable for use in other online system. The entire set of passwords is downloadable for free below with each password being represented as a SHA-1 hash to protect the original value (some passwords contain personally identifiable information) followed by a count of how many times that password had been seen in the source data breaches. The list may be integrated into other systems and used to verify whether a password has previously appeared in a data breach after which a system may warn the user or even block the password outright.

The list contains every wordlist, dictionary, and password database leak that I could find on the internet (and I spent a LOT of time looking). It also contains every word in the Wikipedia databases (pages-articles, retrieved 2010, all languages) as well as lots of books from Project Gutenberg. It also includes the passwords from some low-profile database breaches that were being sold in the underground years ago. The format of the list is a standard text file sorted in non-case-sensitive alphabetical order. Lines are separated with a newline " " character. You can test the list without downloading it by giving SHA256 hashes to the free hash cracker or to @PlzCrack on twitter. Here s a tool for computing hashes easily. Here are the results of cracking LinkedIn s and eHarmony s password hash leaks with the list. The list is responsible for cracking about 30% of all hashes given to CrackStation s free hash cracker, but that figure should be taken with a grain of salt because s

The small business password manager market is experiencing robust growth, driven by increasing cyber threats and the rising adoption of cloud-based services and remote work models. The market's expansion is fueled by the need for enhanced security measures to protect sensitive business data and comply with evolving data privacy regulations like GDPR and CCPA. Small businesses, often lacking dedicated IT resources, find password managers crucial for simplifying password management, improving security posture, and mitigating the risks associated with weak or reused passwords. The market is segmented based on deployment (cloud-based and on-premise), pricing model (subscription and one-time purchase), and functionalities (password generation, storage, sharing, and multi-factor authentication). We estimate the 2025 market size to be approximately $500 million, considering the growth of related cybersecurity markets and the increasing penetration of password management solutions among small businesses. A Compound Annual Growth Rate (CAGR) of 15% is projected for the forecast period (2025-2033), indicating significant market expansion. This growth is expected to be driven by continued technological advancements, increased awareness of cybersecurity risks, and the emergence of innovative password management features such as biometric authentication and dark web monitoring. Factors such as high initial investment costs for robust solutions, the perceived complexity of implementing password managers, and a lack of awareness regarding the benefits of password management among some small businesses present challenges to market growth. However, the increasing affordability of password management solutions, coupled with user-friendly interfaces and educational initiatives promoting cybersecurity best practices, are likely to mitigate these restraints. The competitive landscape is characterized by a mix of established players and emerging startups, each offering varying features, pricing, and deployment models. The market is likely to witness increased consolidation and strategic partnerships in the coming years, as businesses strive to expand their market share and cater to the diverse needs of small businesses. The focus will likely shift towards AI-powered solutions offering enhanced security features, automated password rotation, and proactive threat detection.

Passwords that were leaked or stolen from sites. The Rockyou Dataset is about 14 million passwords.

In the cryptocurrency Bitcoin, users can deterministically derive the private keys used for transmitting money from a password. Such “brain wallets” are appealing because they free users from storing their private keys on untrusted computers. Unfortunately, they also enable attackers to conduct unlimited offline password guessing. In this paper, we report on the first large-scale measurement of the use of brain wallets in Bitcoin. Using a wide range of word lists, we evaluated around 3.9 trillion passwords and passphrases. Surprisingly, after excluding activities by researchers, we identified just 1,730 brain wallets worth around $261K in use from July 2011 to February 2017. We find that all but 21 wallets were drained, usually within 24 hours but often within minutes. We find that around a dozen “drainers” are competing to liquidate brain wallets as soon as they are funded. We find no evidence that users of brain wallets loaded with more bitcoin select stronger passwords, but we do find that brain wallets created with passwords rather than passphrases are cracked more quickly.

For Login DuckDuckGo Please Visit: 👉 DuckDuckGo Login Account

In today’s digital age, privacy has become one of the most valued aspects of online activity. With increasing concerns over data tracking, surveillance, and targeted advertising, users are turning to privacy-first alternatives for everyday browsing. One of the most recognized names in private search is DuckDuckGo. Unlike mainstream search engines, DuckDuckGo emphasizes anonymity and transparency. However, many people wonder: Is there such a thing as a "https://duckduckgo-account.blogspot.com/ ">DuckDuckGo login account ?

In this comprehensive guide, we’ll explore everything you need to know about the DuckDuckGo login account, what it offers (or doesn’t), and how to get the most out of DuckDuckGo’s privacy features.

Does DuckDuckGo Offer a Login Account? To clarify up front: DuckDuckGo does not require or offer a traditional login account like Google or Yahoo. The concept of a DuckDuckGo login account is somewhat misleading if interpreted through the lens of typical internet services.

DuckDuckGo's entire business model is built around privacy. The company does not track users, store personal information, or create user profiles. As a result, there’s no need—or intention—to implement a system that asks users to log in. This stands in stark contrast to other search engines that rely on login-based ecosystems to collect and use personal data for targeted ads.

That said, some users still search for the term DuckDuckGo login account, usually because they’re trying to save settings, sync devices, or use features that may suggest a form of account system. Let’s break down what’s possible and what alternatives exist within DuckDuckGo’s platform.

Saving Settings Without a DuckDuckGo Login Account Even without a traditional DuckDuckGo login account, users can still save their preferences. DuckDuckGo provides two primary ways to retain search settings:

Local Storage (Cookies) When you customize your settings on the DuckDuckGo account homepage, such as theme, region, or safe search options, those preferences are stored in your browser’s local storage. As long as you don’t clear cookies or use incognito mode, these settings will persist.

Cloud Save Feature To cater to users who want to retain settings across multiple devices without a DuckDuckGo login account, DuckDuckGo offers a feature called "Cloud Save." Instead of creating an account with a username or password, you generate a passphrase or unique key. This key can be used to retrieve your saved settings on another device or browser.

While it’s not a conventional login system, it’s the closest DuckDuckGo comes to offering account-like functionality—without compromising privacy.

Why DuckDuckGo Avoids Login Accounts Understanding why there is no DuckDuckGo login account comes down to the company’s core mission: to offer a private, non-tracking search experience. Introducing login accounts would:

Require collecting some user data (e.g., email, password)

Introduce potential tracking mechanisms

Undermine their commitment to full anonymity

By avoiding a login system, DuckDuckGo keeps user trust intact and continues to deliver on its promise of complete privacy. For users who value anonymity, the absence of a DuckDuckGo login account is actually a feature, not a flaw.

DuckDuckGo and Device Syncing One of the most commonly searched reasons behind the term DuckDuckGo login account is the desire to sync settings or preferences across multiple devices. Although DuckDuckGo doesn’t use accounts, the Cloud Save feature mentioned earlier serves this purpose without compromising security or anonymity.

You simply export your settings using a unique passphrase on one device, then import them using the same phrase on another. This offers similar benefits to a synced account—without the need for usernames, passwords, or emails.

DuckDuckGo Privacy Tools Without a Login DuckDuckGo is more than just a search engine. It also offers a range of privacy tools—all without needing a DuckDuckGo login account:

DuckDuckGo Privacy Browser (Mobile): Available for iOS and Android, this browser includes tracking protection, forced HTTPS, and built-in private search.

DuckDuckGo Privacy Essentials (Desktop Extension): For Chrome, Firefox, and Edge, this extension blocks trackers, grades websites on privacy, and enhances encryption.

Email Protection: DuckDuckGo recently launched a service that allows users to create "@duck.com" email addresses that forward to their real email—removing trackers in the process. Users sign up for this using a token or limited identifier, but it still doesn’t constitute a full DuckDuckGo login account.

Is a DuckDuckGo Login Account Needed? For most users, the absence of a DuckDuckGo login account is not only acceptable—it’s ideal. You can:

Use the search engine privately

Customize and save settings

Sync preferences across devices

Block trackers and protect email

—all without an account.

While some people may find the lack of a traditional login unfamiliar at first, it quickly becomes a refreshing break from constant credential requests, data tracking, and login fatigue.

The Future of DuckDuckGo Accounts As of now, DuckDuckGo maintains its position against traditional account systems. However, it’s clear the company is exploring privacy-preserving ways to offer more user features—like Email Protection and Cloud Save. These features may continue to evolve, but the core commitment remains: no tracking, no personal data storage, and no typical DuckDuckGo login account.

Final Thoughts While the term DuckDuckGo login account is frequently searched, it represents a misunderstanding of how the platform operates . Unlike other tech companies that monetize personal data, DuckDuckGo has stayed true to its promise of privacy .

The goal of this study was to measure the attitudes towards data sharing and data-collecting organizations before and after the introduction of the EU General Data Protection regulations (GDPR) among people in Germany. The data come from a three-wave split-panel web survey among people 18 years and older in Germany who were recruited from a German nonprobability online panel. In April 2018 (before the GDPR came into effect), 2,095 participants completed the Wave 1 questionnaire on device ownership, social media use, trust in different data collecting organizations, willingness to share data, general trust, awareness of and knowledge about the GDPR, and privacy concerns. In July and in October 2018 (after the GDPR came into effect), respondents from the earlier waves were invited to participate in a second and a third web survey that repeated most of the questions from the first wave. In addition to participants from the earlier waves, fresh respondents were also invited to Waves 2 and 3. A total of 2,046 (Wave 2) and 2,117 (Wave 3) respondents completed the questionnaire in the subsequent waves. 1,269 participated in all three waves.

Topics:

Wave 1

Possession of smartphone, mobile phone, PC, tablet and/or e-book reader; social media use: account with user name and password at selected providers (Google, Facebook, Twitter, LinkedIn, Xing); trust in institutions (Google, Facebook, Bundesamt für Statistik, Universitätsforscher) with regard to the protection of personal data and reasons for this assessment; probability scale with regard to the protection of personal data at the above-mentioned institutions and reasons for this assessment; agreement with the import of personal data of the social insurance institutions to the survey data; general personal trust; awareness of the EU General Data Protection regulations (GDPR) ; knowledge test: goals of the GDPR (open); feeling of invaded privacy by the following institutions: Google, Facebook, government agencies, university researchers; general privacy concerns.

Wave 2

Possession of smartphone, mobile phone, PC, tablet and/or e-book reader; social media use: account with user name and password with selected providers (Google, Facebook, Twitter, LinkedIn, Xing); trust in institutions (Google, Facebook, Federal Statistical Office, university researchers) with regard to the protection of personal data; general personal trust; awareness of the EU General Data Protection regulations (GDPR); knowledge test: goals of the GDPR (open); consent to the storage of various personal data by Facebook or Google (name, e-mail address, home address, date of birth, telephone number, income, marital status, number of children, current location, Internet browser history, account names from other social media and data received from third parties); feeling of invasion of privacy by the following institutions: Google, Facebook, government agencies, university researchers; general privacy concerns.

Wave 3

Possession of smartphone, mobile phone, PC, tablet and/or e-book reader; social media use: account with user name and password at selected providers (Google, Facebook, Twitter, LinkedIn, Xing); trust in institutions (Google, Facebook, Federal Statistical Office, university researchers) with regard to the protection of personal data; general personal trust; awareness of the EU General Data Protection regulations (GDPR); knowledge test: goals of the GDPR (open); concerns about privacy in general; comprehensibility of excerpts of the contents of the EU General Data Protection regulations (GDPR) (resp. on passenger rights in the event of denied boarding and flight delays); estimated popularity of smartphones (proportion of smartphone owners per 100 adult Germans); repetition of the question on trust data collecting organisations (Google, Facebook) with regard to the protection of personal data and general personal trust; readiness for data exchange by Google (or Facebook or the Federal Statistical Office) for research purposes (or for commercial purposes).

Demography: sex; age (year of birth); federal state; school education; professional qualification.

Additionally coded was: running number; respondent ID; experimental groups GDPR Info; duration (reaction time in seconds); used device type to complete the questionnaire.

The questionnaire also included two experiments, one on the effect of GDPR-related information on trust in data collecting organisations and one on the comfort of data shar...

The most common password encountered over a 45 day period worldwide in 2021 was admin, used nearly 21 million times. Other passwords such as root, nc11, user and enable, were used over three million times.

The encrypted notes app market is experiencing robust growth, driven by increasing concerns over data privacy and security. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $7 billion by 2033. This expansion is fueled by several key factors. The rising adoption of cloud-based services, coupled with the increasing sophistication of cyber threats, is pushing individuals and businesses to seek secure solutions for storing sensitive information. Furthermore, stringent data privacy regulations globally are incentivizing the development and adoption of encrypted note-taking applications that comply with these rules. The market's segmentation includes various features like end-to-end encryption, cross-platform compatibility, and collaboration tools, catering to different user needs. Competition among established players like Evernote and Dropbox alongside innovative startups like Standard Notes and NoteCrypt is driving innovation and improving product offerings. The market’s growth isn't without challenges. The complexity of encryption technology can create a barrier to entry for less tech-savvy users. Price sensitivity and the availability of free or basic alternatives remain factors affecting market penetration. However, ongoing advancements in user interface design and the incorporation of features like biometric authentication are mitigating these concerns. Future growth will likely be propelled by increased integration with other productivity tools, the expansion of AI-powered features (like enhanced search and organization), and the continued focus on improving user experience and accessibility. Geographic expansion into emerging markets with growing digital literacy will also significantly contribute to market expansion. Companies need to focus on building robust security features, clear and intuitive user experiences, and effective marketing strategies to capture a larger market share in this rapidly evolving landscape.

Login Data Set for Risk-Based Authentication

Synthesized login feature data of >33M login attempts and >3.3M users on a large-scale online service in Norway. Original data collected between February 2020 and February 2021.

This data sets aims to foster research and development for Risk-Based Authentication (RBA) systems. The data was synthesized from the real-world login behavior of more than 3.3M users at a large-scale single sign-on (SSO) online service in Norway.

The users used this SSO to access sensitive data provided by the online service, e.g., a cloud storage and billing information. We used this data set to study how the Freeman et al. (2016) RBA model behaves on a large-scale online service in the real world (see Publication). The synthesized data set can reproduce these results made on the original data set (see Study Reproduction). Beyond that, you can use this data set to evaluate and improve RBA algorithms under real-world conditions.

WARNING: The feature values are plausible, but still totally artificial. Therefore, you should NOT use this data set in productive systems, e.g., intrusion detection systems.

Overview

The data set contains the following features related to each login attempt on the SSO:

Feature	Data Type	Description	Range or Example
IP Address	String	IP address belonging to the login attempt	0.0.0.0 - 255.255.255.255
Country	String	Country derived from the IP address	US
Region	String	Region derived from the IP address	New York
City	String	City derived from the IP address	Rochester
ASN	Integer	Autonomous system number derived from the IP address	0 - 600000
User Agent String	String	User agent string submitted by the client	Mozilla/5.0 (Windows NT 10.0; Win64; ...
OS Name and Version	String	Operating system name and version derived from the user agent string	Windows 10
Browser Name and Version	String	Browser name and version derived from the user agent string	Chrome 70.0.3538
Device Type	String	Device type derived from the user agent string	(mobile, desktop, tablet, bot, unknown)1
User ID	Integer	Idenfication number related to the affected user account	[Random pseudonym]
Login Timestamp	Integer	Timestamp related to the login attempt	[64 Bit timestamp]
Round-Trip Time (RTT) [ms]	Integer	Server-side measured latency between client and server	1 - 8600000
Login Successful	Boolean	True: Login was successful, False: Login failed	(true, false)
Is Attack IP	Boolean	IP address was found in known attacker data set	(true, false)
Is Account Takeover	Boolean	Login attempt was identified as account takeover by incident response team of the online service	(true, false)

Data Creation

As the data set targets RBA systems, especially the Freeman et al. (2016) model, the statistical feature probabilities between all users, globally and locally, are identical for the categorical data. All the other data was randomly generated while maintaining logical relations and timely order between the features.

The timestamps, however, are not identical and contain randomness. The feature values related to IP address and user agent string were randomly generated by publicly available data, so they were very likely not present in the real data set. The RTTs resemble real values but were randomly assigned among users per geolocation. Therefore, the RTT entries were probably in other positions in the original data set.

• The country was randomly assigned per unique feature value. Based on that, we randomly assigned an ASN related to the country, and generated the IP addresses for this ASN. The cities and regions were derived from the generated IP addresses for privacy reasons and do not reflect the real logical relations from the original data set.

• The device types are identical to the real data set. Based on that, we randomly assigned the OS, and based on the OS the browser information. From this information, we randomly generated the user agent string. Therefore, all the logical relations regarding the user agent are identical as in the real data set.

• The RTT was randomly drawn from the login success status and synthesized geolocation data. We did this to ensure that the RTTs are realistic ones.

Regarding the Data Values

Due to unresolvable conflicts during the data creation, we had to assign some unrealistic IP addresses and ASNs that are not present in the real world. Nevertheless, these do not have any effects on the risk scores generated by the Freeman et al. (2016) model.

You can recognize them by the following values:

• ASNs with values >= 500.000

• IP addresses in the range 10.0.0.0 - 10.255.255.255 (10.0.0.0/8 CIDR range)

Study Reproduction

Based on our evaluation, this data set can reproduce our study results regarding the RBA behavior of an RBA model using the IP address (IP address, country, and ASN) and user agent string (Full string, OS name and version, browser name and version, device type) as features.

The calculated RTT significances for countries and regions inside Norway are not identical using this data set, but have similar tendencies. The same is true for the Median RTTs per country. This is due to the fact that the available number of entries per country, region, and city changed with the data creation procedure. However, the RTTs still reflect the real-world distributions of different geolocations by city.

See RESULTS.md for more details.

Ethics

By using the SSO service, the users agreed in the data collection and evaluation for research purposes. For study reproduction and fostering RBA research, we agreed with the data owner to create a synthesized data set that does not allow re-identification of customers.

The synthesized data set does not contain any sensitive data values, as the IP addresses, browser identifiers, login timestamps, and RTTs were randomly generated and assigned.

Publication

You can find more details on our conducted study in the following journal article:

Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service (2022)
Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono.
ACM Transactions on Privacy and Security

Bibtex

@article{Wiefling_Pump_2022,
 author = {Wiefling, Stephan and Jørgensen, Paul René and Thunem, Sigurd and Lo Iacono, Luigi},
 title = {Pump {Up} {Password} {Security}! {Evaluating} and {Enhancing} {Risk}-{Based} {Authentication} on a {Real}-{World} {Large}-{Scale} {Online} {Service}},
 journal = {{ACM} {Transactions} on {Privacy} and {Security}},
 doi = {10.1145/3546069},
 publisher = {ACM},
 year  = {2022}
}

License

This data set and the contents of this repository are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. See the LICENSE file for details. If the data set is used within a publication, the following journal article has to be cited as the source of the data set:

Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono: Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service. In: ACM Transactions on Privacy and Security (2022). doi: 10.1145/3546069

1. Few (invalid) user agents strings from the original data set could not be parsed, so their device type is empty. Perhaps this parse error is useful information for your studies, so we kept these 1526 entries.↩︎

Introduction

Training.gov.au (TGA) is the National Register of Vocational Education and Training in Australia and contains authoritative information about Registered Training Organisations (RTOs), Nationally Recognised Training (NRT) and the approved scope of each RTO to deliver NRT as required in national and jurisdictional legislation.

TGA web-services overview

TGA has a web service available to allow external systems to access and utilise information stored in TGA through an external system. The TGA web service is exposed through a single interface and web service users are assigned a data reader role which will apply to all data stored in the TGA.

The web service can be broadly split into three categories:

1. RTOs and other organisation types;

2. Training components including Accredited courses, Accredited course Modules Training Packages, Qualifications, Skill Sets and Units of Competency;

3. System metadata including static data and statistical classifications.

Users will gain access to the TGA web service by first passing a user name and password through to the web server. The web server will then authenticate the user against the TGA security provider before passing the request to the application that supplies the web services.

There are two web services environments:

1. Production - ws.training.gov.au – National Register production web services

2. Sandbox - ws.sandbox.training.gov.au – National Register sandbox web services.

The National Register sandbox web service is used to test against the current version of the web services where the functionality will be identical to the current production release. The web service definition and schema of the National Register sandbox database will also be identical to that of production release at any given point in time. The National Register sandbox database will be cleared down at regular intervals and realigned with the National Register production environment.

Each environment has three configured services:

1. Organisation Service;

2. Training Component Service; and

3. Classification Service.

Sandbox environment access

To access the download area for web services, navigate to http://tga.hsd.com.au and use the below name and password:

Username: WebService.Read (case sensitive)

Password: Asdf098 (case sensitive)

This download area contains various versions of the following artefacts that you may find useful

• Training.gov.au web service specification document;

• Training.gov.au logical data model and definitions document;

• .NET web service SDK sample app (with source code);

• Java sample client (with source code);

• How to setup web service client in VS 2010 video; and

• Web services WSDL's and XSD's.

For the business areas, the specification/definition documents and the sample application is a good place to start while the IT areas will find the sample source code and the video useful to start developing against the TGA web services.

The web services Sandbox end point is: https://ws.sandbox.training.gov.au/Deewr.Tga.Webservices

Production web service access

Once you are ready to access the production web service, please email the TGA team at tgaproject@education.gov.au to obtain a unique user name and password.