opendp · TedTed · Oct 30, 2025 · Oct 30, 2025
diff --git a/doc/index.rst b/doc/index.rst
@@ -97,7 +97,7 @@ If you have any questions, feedback, or feature requests, please `let us know on
 
 The Tumult Analytics documentation introduces all of the concepts necessary to get started producing differentially private results.
 Users who wish to learn more about the fundamentals of differential privacy can consult
-`this blog post series <https://desfontain.es/privacy/friendly-intro-to-differential-privacy.html>`__
+`this blog post series <https://desfontain.es/blog/friendly-intro-to-differential-privacy.html>`__
 or `this longer introduction <https://scholarship.law.vanderbilt.edu/jetlaw/vol21/iss1/4/>`__.
 
 ..

diff --git a/doc/topic-guides/privacy-budgets.rst b/doc/topic-guides/privacy-budgets.rst
@@ -22,22 +22,22 @@ Tumult Analytics currently supports three distinct privacy definitions:
 
 * *Pure differential privacy ("pure DP", or simply "DP")*, with its associated privacy parameter ``epsilon``.
   For data publication use cases, the value of ``epsilon`` is often chosen to be
-  `lower than 5 <https://desfontain.es/privacy/real-world-differential-privacy.html>`_.
+  `lower than 5 <https://desfontain.es/blog/real-world-differential-privacy.html>`_.
   Values below 1 are typically considered conservative. Pure DP is the
   original definition of differential privacy. To learn more, consult this
-  `blog post <https://desfontain.es/privacy/differential-privacy-in-more-detail.html>`__.
+  `blog post <https://desfontain.es/blog/differential-privacy-in-more-detail.html>`__.
 * *Approximate differential privacy ("Approx DP")*, with its associated
   privacy parameters ``epsilon`` and ``delta``. Approximate DP is a relaxation of PureDP where mechanisms are
   allowed to fail to provide Pure DP guarantees with some (hopefully small) probability,``delta``. Delta can
   be thought of as that failure probability. The most common advice is to set ``delta`` significantly smaller
   smaller than ``1/n``, where ``n`` is the number of people in your dataset. The relaxation of Pure DP
   enables new types of queries and can therefore be a powerful tool that still offers reasonable privacy
   protections when used correctly. To learn more, consult this
-  `blog post <https://desfontain.es/privacy/almost-differential-privacy.html>`__.
+  `blog post <https://desfontain.es/blog/almost-differential-privacy.html>`__.
 * *Zero-concentrated differential privacy ("zCDP")*, with its associated
   privacy parameter ``rho``. zCDP is a variant of differential privacy, which
   adjusts the way privacy loss is measured. To learn more, consult this:
-  `blog post <https://desfontain.es/privacy/renyi-dp-zero-concentrated-dp.html>`__.
+  `blog post <https://desfontain.es/blog/renyi-dp-zero-concentrated-dp.html>`__.
 
 Queries with higher values of epsilon, delta, and rho will produce results with
 less randomization that are therefore more accurate, whereas
@@ -147,7 +147,7 @@ their suspicion, whereas larger epsilons allow the attacker to determine with in
 certainty whether or not their target is in the database.
 
 For an even more in-depth explanation of this topic, you can check out the following
-`blog post <https://desfontain.es/privacy/differential-privacy-in-more-detail.html>`__.
+`blog post <https://desfontain.es/blog/differential-privacy-in-more-detail.html>`__.
 
 The impact of data size
 ^^^^^^^^^^^^^^^^^^^^^^^

diff --git a/doc/topic-guides/understanding-sensitivity.rst b/doc/topic-guides/understanding-sensitivity.rst
@@ -228,7 +228,7 @@ Analytics, it is certainly not exhaustive. If you have additional questions, fee
 to reach out to us on 
 `our Slack server <https://join.slack.com/t/opendp/shared_invite/zt-1aca9bm7k-hG7olKz6CiGm8htI2lxE8w>`_!
 
-.. _blog post: https://desfontain.es/privacy/gaussian-noise.html
+.. _blog post: https://desfontain.es/blog/gaussian-noise.html
 
 
 

diff --git a/doc/tutorials/first-steps.rst b/doc/tutorials/first-steps.rst
@@ -28,7 +28,7 @@ accomplish common tasks. To learn more about the trade-offs involved in
 parameter setting and mechanism design, you can consult our
 :ref:`topic guides <topic-guides>`.
 
-.. _differential privacy: https://desfontain.es/privacy/friendly-intro-to-differential-privacy.html
+.. _differential privacy: https://desfontain.es/blog/friendly-intro-to-differential-privacy.html
 
 Setup
 -----

diff --git a/doc/tutorials/privacy-budget-basics.rst b/doc/tutorials/privacy-budget-basics.rst
@@ -29,9 +29,9 @@ fundamentals, you can consult the following resources.
 - If you would like to know what privacy parameters are commonly used for data
   publication, you can consult this `list of real-world use cases`_.
 
-.. _explainer: https://desfontain.es/privacy/differential-privacy-in-more-detail.html
+.. _explainer: https://desfontain.es/blog/differential-privacy-in-more-detail.html
 
-.. _list of real-world use cases: https://desfontain.es/privacy/real-world-differential-privacy.html
+.. _list of real-world use cases: https://desfontain.es/blog/real-world-differential-privacy.html
 
 These are only optional reading! The one-sentence summary above (smaller budget
 = better privacy) is enough to follow the rest of this tutorial. Let's get

diff --git a/src/tmlt/analytics/privacy_budget.py b/src/tmlt/analytics/privacy_budget.py
@@ -230,7 +230,7 @@ class ApproxDPBudget(PrivacyBudget):
 
     This privacy definition is also known as (ε, δ)-differential privacy, and the
     associated privacy parameters are epsilon and delta. The formal definition can
-    be found `here <https://desfontain.es/privacy/almost-differential-privacy.html#formal-definition>`__.
+    be found `here <https://desfontain.es/blog/almost-differential-privacy.html#formal-definition>`__.
     """  # pylint: disable=line-too-long
 
     _epsilon: ExactNumber