Do You Know Your VPN’s Jurisdiction? Your Privacy Depends on It


When shopping for a virtual private network, you’re probably looking into things like VPN protocols, price, speeds, streaming capabilities and other features before deciding which one to go with. All are important factors to consider when looking for a VPN, but one crucial consideration often gets overlooked: jurisdiction.

Jurisdiction refers to the country where the VPN company is officially registered and to which country’s laws the VPN is beholden. Because privacy laws and data retention regulations differ greatly from one country to the next, jurisdiction has major privacy implications for VPN users. 

How major? I’d say using a VPN based in a country whose laws require VPNs to log user data is worse for your privacy than using no VPN at all. Same thing if a country’s laws allow local or foreign intelligence agencies to compel companies to log and share user data. Those are two of the biggest red flags you can find in a VPN service and big reasons why I’ve always paid close attention to jurisdiction throughout my decade-plus of experience testing and reviewing VPNs. 

Jurisdiction is a complex issue that can often be difficult to dissect, but I always make sure that any VPN service I recommend is based in a jurisdiction where it can’t be forced to spy on its users. Unfortunately, there’s still a lot of confusion about how local laws do or do not apply to VPN companies and what authority foreign agencies may or may not have over VPNs in other countries.

What really matters for your privacy is making sure the VPN you’re using is trustworthy, with a regularly audited no-logs policy, and is based in a privacy-friendly jurisdiction with no data retention laws that could force VPNs to log user data. Bonus points if the VPN is open-source and its no-logs claims have been tested in the wild.

The number of Eyes isn’t the most important detail

A long-held belief among many in online circles is that it’s risky to use a VPN based in a 14 Eyes country, which is a group of 14 countries that share surveillance data under an intelligence alliance.

But what actually matters for your privacy is using a VPN based in a country that doesn’t have mandatory data retention laws that could allow authorities to compel VPN companies to log user traffic. The lack of such regulations is what really allows a VPN to claim a genuine no-logs policy and is true whether the VPN is based in a 14 Eyes country or not. 

In other words, the local regulatory landscape has a much greater influence than any Eyes designation in dictating whether a VPN is safe to use. 

Case in point: Mullvad, one of the most private VPNs available and one that I regularly recommend for users with critical privacy needs, is based in Sweden, one of the 14 Eyes countries. But the legal framework in Sweden is such that authorities are unable to compel VPN companies to log user data. Mullvad answers to Swedish law and Swedish law only, meaning that intelligence agencies from another 14 Eyes country (or any other country, for that matter) have no power to jump in and make Mullvad log user data. 

Also, Mullvad is fully open-source and has a no-logs policy that has been audited, offering a high level of transparency and peace of mind that the company isn’t logging user activity on its network. Further, Mullvad says that it retains lawyers to monitor the legal landscape (in Sweden and abroad) and is prepared to shut down its service if a government becomes legally able to compel the company to spy on its users.

In fact, Mullvad’s policies were put to the test in 2023 when Swedish authorities, acting on a search warrant, raided Mullvad’s offices in Gothenburg to seize customer data on the VPN’s systems. However, the Swedish police left empty handed because the data did not exist.

Similarly, Windscribe, also based in a 14 Eyes country (Canada), maintains air-tight privacy and isn’t subject to laws that would force them to log user data. Windscribe has been tested a few times in the wild — once by Greek authorities in 2023, who later dropped their case in 2025 due to lack of data, and more recently by Dutch authorities, who reportedly seized a Windscribe server in February. The Dutch case is still ongoing as of this writing, but Windscribe CEO Yegor Sak told me that no user data is at risk because there is no user data to hand over.

In many jurisdictions (in or out of the 14 Eyes), authorities may be able to legally approach VPN companies with a warrant, demanding they hand over existing data related to an active investigation. But if the VPN is truly not logging customer data, it won’t have anything of use to hand over to authorities. 

But in certain jurisdictions, like in the United States, authorities can issue a subpoena, warrant or other legal action that includes a gag order, which can prevent a VPN company from disclosing the fact it has been told to start logging user data. Additionally, Wired reported that United States lawmakers recently sent a letter to the US director of intelligence, asking for confirmation on whether VPN users in the US are essentially waiving their constitutional protections from warrantless government surveillance when connecting to a server overseas. If the answer is yes, that could be a major issue if you’re using a shady VPN service that’s collecting data on your internet activity or if your VPN can be compelled by a legal order to start logging.

However, a trustworthy VPN that’s built from the ground up for privacy can’t just flip a switch and start logging from one minute to the next. Complying with such an order would require that VPN to modify its server code and essentially its entire infrastructure design to start recording useful data and storing it permanently — not to mention totally selling out its entire user base in the process.

This is exactly why things like RAM-only servers, open source software, transparency reports and regular third-party audits are so important in addition to jurisdiction. A RAM-only server infrastructure helps ensure that no data persists on a hard drive and that all data is completely wiped whenever a server is shut down or rebooted. If a VPN’s apps are open source, its source code is publicly available for anyone to scrutinize, meaning that any attempt at secret logging could be apparent to someone reviewing it. 

Transparency reports that detail the number and type of legal requests a VPN receives in a certain timeframe (and how the company responded to the requests, if at all) are important in building public trust. And although independent audits don’t paint the full picture, they’re crucial trust signals that can help validate a VPN’s claims that they’re not logging and that their infrastructure is properly set up to protect user privacy. 

A VPN with a reasonable privacy setup would struggle to start spying on users, even if it could be compelled to do so. But the point of good VPN jurisdiction is that it shouldn’t be able to.

Where would (and wouldn’t) you want your VPN to be based

Generally speaking, you’ll want a VPN that’s based in a jurisdiction without mandatory data retention laws, supported instead by strong data protection frameworks that have the proper checks in place to limit government overreach and warrants from other countries. Some of the best jurisdictions for VPNs to be in include countries like Switzerland (Proton VPN), British Virgin Islands (ExpressVPN), Panama (NordVPN), Sweden (Mullvad), Gibraltar and Romania. 

Privacy-focused VPN users should think twice about going with a VPN based in the US due to the risks associated with VPN companies being served national security letters (which can compel a company to hand over records) and gag orders preventing them from talking about it.

VPNs based in the UK are also risky because the country’s Investigatory Powers Act gives the government the authority to weaken encryption, enforce gag orders and compel ISPs and potentially VPNs to log user data. Similar laws in Australia make VPNs based there risky as well.

VPNs based in countries with heavy internet censorship and surveillance should never be considered. For example, any VPN operating in China needs to be government-approved and provide authorities with backdoor access to its systems.   

Look for VPNs with clear jurisdiction

While many VPNs are incorporated and operate in a single jurisdiction, others may operate out of one country but set up a legally registered entity in a different jurisdiction. This may be done for tax benefits or to ensure that the VPN company is legally registered in a safe jurisdiction, even if it doesn’t operate physically in that country. 

Also, some VPN parent companies may be headquartered in an entirely different country. For instance, ExpressVPN’s parent company, Kape Technologies, is a UK-based company, but ExpressVPN is legally based out of the British Virgin Islands. ExpressVPN makes clear in its privacy policy that it operates in accordance with BVI laws. Similarly, NordVPN’s offices are in Lithuania, but under its Panamanian jurisdiction, all data requests “must follow the appropriate legal process set out under the laws of the Republic of Panama,” according to the company’s privacy policy.

Because of all of this, VPN ownership structures and actual jurisdiction can sometimes be a tough nut to crack. But trustworthy VPNs all make it clear what jurisdiction they are legally registered in and, therefore, what country’s laws they answer to. It’s something CNET specifically looks for when evaluating VPNs. If you come across a provider that doesn’t make its ownership or jurisdiction clear, it’s best to avoid that VPN. 

Bottom line

Ultimately, what you want is a VPN that’s built for privacy from the ground up and is based in a country that won’t force it to spy on its users — that’s the real consideration when it comes to jurisdiction.

If privacy is your main consideration with a VPN, you can also read up on the settings to enable for optimal privacy and additional privacy and security tools to bundle with your VPN, or check out CNET’s reviews of Mullvad, ExpressVPN and Proton VPN.





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What Are Selection Techniques

Selection techniques in machine learning help in reducing the noise by taking in only the relevant data after the pre-processing. The techniques have the ability to choose the relevant variables according to the type of user’s problem. In case any data comes up that is not relevant to the requirement, it tends to slow down the efficiency process of the model and also decrease the accuracy. Therefore, it is very important to have appropriate feature selection techniques for the models in order to have better outcomes and accuracy. 

The main idea of working with selection techniques is to manually extract the relevant settings from the parent set to have high-accuracy model structures.

Feature Selection in Machine learning

The techniques are divided into the category of supervised and unsupervised learning. These two categories are further divided into 4 main methods for selecting the features.

Filter Method :

There are statistical ways for selecting the features using the filter method. The features are selected in the pre-processing stage as there is no learning process involved in this. The aim of this approach is to filter out the unrequired and irrelevant features by using matrices and ranking methods. The most important advantage of using the filter method is that it does not overfit the data.

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Wrapper Method :

In this method, a user makes different combinations that are evaluated or compared with a lot of other possible combinations. In this way, the feature selection is done. A subset of features is selected and the algorithm is trained based on the subset. The output of the algorithm then decides if the features will be added or not. This method is further based on 4 types which are:

  • Forward Selection : This process takes in an empty feature set. It keeps adding a feature to each interaction and checks the progress simultaneously as if it is improving or not. This method keeps on iterating unless there comes a feature that does not improve the progress of the model.
  • Backward Elimination : This approach is the complete opposite of the forward selection approach. The process takes in all the features of the algorithm and then keeps removing a feature one by one on each iteration. It checks the progress simultaneously as if it is improving or not. This method keeps on iterating unless there comes a feature that does not improve the progress of the model.
  • Exhaustive Feature Selection : It is the most common approach for feature selection as each feature is set as brute-force. The approach aims to try various combinations of features in order to give the best outcome.
  • Recursive Feature Elimination : This method is based on the greedy approach as its features are selected in a smaller amount. An estimator is made to test every set of features designed and thus we get an outcome of the best features.
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Embedded Method :

This is a great method for feature selection as it has the advantages for both filter and wrapper methods collectively. The processing time in the embedded method is very high just like the filter method, however, they provide more accurate outcomes.

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There are a few techniques involved with embedded methods which are:

  • Regularisation : This aims at regularising the feature selection method simply by adding a penalty if the data gets overfitted in the model. The points shrink to a value of 0 and they are eliminated from the dataset. The types of regularizations are L1, L2, L3, etc. 
  • Random Forest Importance : This technique involves a lot of tree-based approaches to select the features for an algorithm. A number of decision trees are involved in this as the ranking of nodes is performed in all the trees to get the results. After filtering out the irrelevant nodes, a subset of the most relevant nodes creates a final selection of features.
Hybrid Method :

This approach takes in features as small-sized samples. The main idea is to select the features using instance learning. The features that correspond to the instances are selected as they are relevant to the algorithm.

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Feature Selection Models

Supervised Model :

This model is defined as the class of machine learning methodologies where the user can train with the help of continuous and well-labelled data. For instance, the data can be historical data where the user wishes to predict whether a customer will take a loan or not. Supervised algorithms tend to train over the well-structured data after the preprocessing and feature characterization of this labelled data. It is further tested on a completely new data point for the prediction of a loan defaulter. The most popular supervised learning algorithms are the k-nearest neighbour algorithm, linear regression algorithm, logistic regression, decision tree, etc.

This is further divided into 2 categories:

  • Regression: The dealing of output variables is done using regressions as it includes graphs, images, etc. For example to determine age, height, etc. 
  • Classification: it helps in classifying different objects such as yellow, orange, wrong or right, etc.
Unsupervised Model

This model is defined as a class of machine learning methodologies where the tasks are performed using the unlabelled data. Clustering is the most popular use case for unsupervised algorithms. It is defined as the process of grouping similar data points together without manual intervention. The most popular unsupervised learning algorithms are k-means, k-medoids, etc. 

This is further divided into 2 categories:

  • Clustering :This means when the machine requires an inherent group while training the data.
  • Association :This category has a set of rules which helps in the identification of massive data. For example, a list of students who could be interested in artificial intelligence as well as machine learning.
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How To Choose a Feature Selection Model

It is very important for machine learning engineers as well as researchers to understand which feature selection model is most suitable for them. The most data types are known by the engineer, the easier it will be for him to choose properly and wisely. This whole concept is based on 4 main approaches which are:

  • Numerical Input, Numerical Output : There are two methods used in this technique which are Pearson’s correlation coefficient and Spearman’s Rank Coefficient.  The numerals are basically used for the prediction of regression models for continuous numerical such as int, float, etc. 
  • Numerical Input, Categorical Output : There are two methods used in this technique which are the ANOVA correlation coefficient, and Kendall’s rank coefficient. The numerals are basically used for the classification of predictive models for continuous numerical such as int, float, etc. 
  • Categorical Input, Numerical Output : This is a case of the prediction of regression models using input based on categories. The process is the same as numerical input, and categorical output but in a reverse fashion. 
  • Categorical Input, Categorical Output : This is a case of classification of predictive models using both categorical inputs as well as outputs. The main approach affiliated with this method is the Chi-squared method. Moreover, information gain can also be used with this technique.

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Conclusion:

The process of selecting features in machine learning is a vast concept and it involves a lot of research to select the best features. However there is no hard and fast rule for making the selection, it all depends on the type of model and its algorithm and how a machine learning engineer wants to pursue it. Selection techniques in machine learning help in reducing the noise by taking in only the relevant data after the pre-processing. 

In this article, we have talked about various feature selection methods that use certain algorithms for making the best possible outcomes and why we should make this feature selection method. Along with this, we have talked about how we can finalise the best feature selection model to work with.

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