add data privacy blogpost

Author: cyyeh

packages/doc/blog/data-privacy-mechanisms.mdx

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+---
+authors:
+    name: Jimmy Yeh
+    title: core member of VulcanSQL
+    url: https://github.com/cyyeh
+    image_url: https://avatars.githubusercontent.com/u/11023068?v=4
+    email: jimmy.yeh@cannerdata.com
+---
+
+# Data Privacy Mechanisms provided by VulcanSQL for Easier Data Sharing
+
+![cover](./static/cover-data-privacy.jpg)
+
+## What is data sharing and why it's important?
+
+Data sharing is a behavior that we share data to other parties 
+which maybe other departments in the same company, or customers outside the company, etc.
+The reason why we agree to share data is that we finally realize data is a valuable asset 
+especially to business, since it can make the business process more smooth and enable better decision making results!
+
+When thinking about how to share data with others, there are some common requirements coming up to our minds, such as 
+what are the formats of data being stored, what kind of storage should persist the data, and how to share data with other parties. 
+However, there are also other directions we need to consider and I would like to discuss data privacy with you in this article.
+
+## Why data privacy is important to data sharing?
+
+In terms of sharing data with others, there are certainly some scenraios that we can treat everyone we share with equally.
+<!--truncate-->
+For example, datasets found in Kaggle competitions are normally the same to all competition participants.
+However, in the business world, we should especially obey the principle of least privilege(PoLP). It's an information security concept which maintains that a user 
+or entity should only have access to the specific data, resources and applications needed to complete a required task[^1].
+
+Given that we understand data sharing is a great opportunity to share valuable resources with others, 
+and we also realize we need to control how different parts of data can be accessed from different parties. 
+Now the question comes to how do we share data with others in a more contrallable way that can fulfill the data privacy requirement?
+
+
+## How VulcanSQL can help?
+
+VulcanSQL comes with several built-in data privacy mechanisms to enable a more controllable and scalable data sharing use case!
+
+As of now, VulcanSQL has 5 techniques for handling data privacy, here are some brief introduction to each technique and [in the
+Showcase section](#showcase), we'll show you the code and explain further accordingly.
+
+### Authentication
+
+Authentication is the first layer of data privacy protection. Any entity that is not authenticated is not allowed to access any resources.
+At the moment, VulcanSQL only accepts three authentication methods, namely [HTTP Basic](../docs/data-privacy/authn#http-basic), 
+[password file](../docs/data-privacy/authn#password-file) and [simple token](../docs/data-privacy/authn#simple-token). Since we realize there should be
+a more mature and easy way for users to authenticate, we plan to support OpenID Connect in the future.
+
+:::info
+You can understand more about [the authentication mechanism in VulcanSQL here](../docs/data-privacy/authn)!
+:::
+
+### Authorization
+
+With authorization, VulcanSQL applies an attribute-based access control(ABAC) approach to control access 
+based on user attributes provided by Authenticator. In VulcanSQL, we can configure each user's attributes in `vulcan.yaml`;
+then we can define different policies for different users based on their attributes for each data source in `profiles.yaml`.
+With this mechanism, different users would see different parts of the data based on their attributes defined in VulcanSQL!
+
+:::info
+You can understand more about [the authorization mechanism in VulcanSQL here](../docs/data-privacy/authz)!
+:::
+
+### Dynamic Data Masking
+
+Sometimes, we want to share masked data to users. The purpose is to protect the actual data while having a functional substitute 
+for occasions when the real data is not required!
+
+With dynamic data masking, we can define a specific pattern for masking the real data, such as transforming an ID from F123456789 to F12xxxx89
+using a `partial(3, 'xxxx', 2)` function.
+
+:::info
+You can understand more about [the dynamic data masking mechanism in VulcanSQL here](../docs/data-privacy/data-masking)!
+:::
+
+### Column-level Security
+
+If we need to have fine grained control over some specific columns, we can use the Column-level Security(CLS) mechanism to achieve the goal.
+In VulcanSQL, we can decide who can access the specific column based on their user attributes.
+
+:::info
+You can understand more about [the column-level security mechanism in VulcanSQL here](../docs/data-privacy/cls)!
+:::
+
+### Row-level Security
+
+Similar to the case of Column-level Security, if we need to have fine grained control over some specific rows, we can use the Row-level Security(RLS) mechanism to achieve the goal.
+In VulcanSQL, we can decide who can access the specific row based on their user attributes.
+
+:::info
+You can understand more about [the row-level security mechanism in VulcanSQL here](../docs/data-privacy/rls)!
+:::
+
+## Showcase
+
+Now we are going to show you the code to demonstrate how you can deliver data privacy mechanisms in SQL templates!
+If you would like to read the source code or try the example by yourself, 
+welcome to [check it out here](https://github.com/Canner/vulcan-sql-examples/tree/main/data-sharing)!
+
+Below is the sample dataset we'll use in the showcase:
+
+|id|department|last_name|company_role|annual_salary|
+|---|---|---|---|---|
+|JDK32424|engineering|James|engineer|"$100,000"|
+|EKJ34124|sales|Harden|sales|"$120,000"|
+|MKO56124|sales|Michael|manager|"$110,000"|
+|ONP01124|engineering|Cindy|manager|"$115,000"|
+|NZP59124|ceo|Rosa|boss|"$150,000"|
+
+Below is the code you may write in SQL templates in VulcanSQL:
+
+```sql
+SELECT
+    -- dynamic data masking
+    {% masking id partial(2, 'xxxxxxx', 2) %} as id,
+    department,
+    last_name,
+    company_role,
+    -- column level security
+    {% if context.user.attr.role == 'employer' %}
+        annual_salary
+    {% else %}
+        NULL AS annual_salary
+    {% endif %}
+FROM read_csv_auto('departments.csv', HEADER=True)
+-- row level security
+{% if context.user.attr.role != 'employer' %}
+    WHERE department = {{ context.user.attr.department }}
+{% endif %}
+```
+
+Here is the `auth` configuration in `vulcan.yaml`:
+
+```yaml
+auth:
+  enabled: true
+  options:
+    basic:
+      # Read users and passwords from a text file.
+      htpasswd-file:
+        path: passwd.txt # Path to the password file.
+        users: # (Optional) Add attributes for users
+          - name: james
+            attr:
+              role: employee
+              department: engineering
+          - name: harden
+            attr:
+              role: employee
+              department: sales
+          - name: michael
+            attr:
+              role: employee
+              department: sales
+          - name: cindy
+            attr:
+              role: employee
+              department: engineering
+          - name: rosa
+            attr:
+              role: employer
+              department: ceo
+```
+
+The REST API results you'll see based on different users:
+
+**James**
+
+|id|department|last_name|company_role|annual_salary|
+|---|---|---|---|---|
+|JDxxxxxxx24|engineering|James|engineer||	
+|ONxxxxxxx24|engineering|Cindy|manager||
+
+**Harden**
+
+|id|department|last_name|company_role|annual_salary|
+|---|---|---|---|---|
+|EKxxxxxxx24|sales|Harden|sales||	
+|MKxxxxxxx24|sales|Michael|manager||
+
+**Michael**
+
+|id|department|last_name|company_role|annual_salary|
+|---|---|---|---|---|
+|EKxxxxxxx24|sales|Harden|sales||	
+|MKxxxxxxx24|sales|Michael|manager||
+
+**Cindy**
+
+|id|department|last_name|company_role|annual_salary|
+|---|---|---|---|---|
+|JDxxxxxxx24|engineering|James|engineer||	
+|ONxxxxxxx24|engineering|Cindy|manager||
+
+**Rosa**
+
+|id|department|last_name|company_role|annual_salary|
+|---|---|---|---|---|
+|JDxxxxxxx24|engineering|James|engineer|$100,000|
+|EKxxxxxxx24|sales|Harden|sales|$120,000|
+|MKxxxxxxx24|sales|Michael|manager|$110,000|
+|ONxxxxxxx24|engineering|Cindy|manager|$115,000|
+|NZxxxxxxx24|ceo|Rosa|boss|$150,000|
+
+After observing the result tables shown above and the given SQL templates,
+we can clearly figure out several data privacy mechanisms provided by VulcanSQL:
+
+1. Authentication: In the above example, we used HTTP Basic as the authentication method 
+and the password of each user was stored in a text file called `passwd.txt`.
+2. Authorization: You can find that we had defined user attributes in `vulcan.yaml`.
+With these user attributes defined, we can have more fine grained control on what kind of data
+each user should access.
+3. Dynamic Data Masking: `{% masking id partial(2, 'xxxxxxx', 2) %} as id` makes only the first two and
+last two digits of id visible, and the rest is masked.
+4. Column-level Security: We can see that only the user who is in the employer role can see the salary field, 
+so Rosa is the only one in this role.
+5. Row-level Security: We can see a user who is not in the employer role can only see the data
+in the same department as he/she.
+
+## Conclusion
+
+Data privacy is more important than ever. We may regard it as a special kind of human rights, 
+so we should protect the data from being abused!
+
+We hope this blog post effectively highlights the significance of data privacy when sharing data with other parties.
+It also showcases that VulcanSQL offers user-friendly solutions that are certainly worth exploring.
+
+[^1]: The definition of the principle of least privilege(PoLP) is referenced from the [article from paloalto networks](https://www.paloaltonetworks.com/cyberpedia/what-is-the-principle-of-least-privilege#:~:text=The%20principle%20of%20least%20privilege%20(PoLP)%20is%20an%20information%20security,to%20complete%20a%20required%20task.).