docs(frontend): rename security_curves in security

.gitbook.yaml

@@ -48,5 +48,6 @@ redirects:
   dev/compilation/TracingDialect: explanations/TracingDialect.md
   dev/compilation/RTDialect: explanations/RTDialect.md
   dev/compilation/SDFGDialect: explanations/SDFGDialect.md
-  dev/security/security_curves: explanations/security_curves.md
+  dev/security/security_curves: explanations/security.md
+  explanations/security_curves: explanations/security.md
   dev/setup/layout: explanations/layout.md

docs/SUMMARY.md

@@ -74,7 +74,7 @@
   * [Tracing dialect](explanations/TracingDialect.md)
   * [Runtime dialect](explanations/RTDialect.md)
   * [SDFG dialect](explanations/SDFGDialect.md)
-* [Security](explanations/security_curves.md)
+* [Security](explanations/security.md)
 * [Call FHE circuits from other languages](explanations/call_from_other_language.md)
 * [Project layout](explanations/layout.md)
 

docs/explanations/security_curves.md → docs/explanations/security.md

@@ -5,17 +5,17 @@
 To select secure cryptographic parameters for usage in Concrete, we utilize the [Lattice-Estimator](https://github.com/malb/lattice-estimator). In particular, we use the following workflow:
 
 1. Data Acquisition
-    - For a given value of $$(n, q = 2^{64}, \sigma)$$ we obtain raw data from the Lattice Estimator, which ultimately leads to a security level $$\lambda$$. All relevant attacks in the Lattice Estimator are considered. 
-    - Increase the value of $$\sigma$$, until the tuple $$(n, q = 2^{64}, \sigma)$$ satisfies the target level of security $$\lambda_{target}$$. 
+    - For a given value of $$(n, q = 2^{64}, \sigma)$$ we obtain raw data from the Lattice Estimator, which ultimately leads to a security level $$\lambda$$. All relevant attacks in the Lattice Estimator are considered.
+    - Increase the value of $$\sigma$$, until the tuple $$(n, q = 2^{64}, \sigma)$$ satisfies the target level of security $$\lambda_{target}$$.
     - Repeat for several values of $$n$$.
 
-2. Model Generation for $$\lambda = \lambda_{target}$$. 
+2. Model Generation for $$\lambda = \lambda_{target}$$.
     - At this point, we have several sets of points $$\{(n, q = 2^{64}, \sigma)\}$$ satisfying the target level of security $$\lambda_{target}$$. From here, we fit a model to this raw data ($$\sigma$$ as a function of $$n$$).
 
 3. Model Verification.
-    - For each model, we perform a verification check to ensure that the values output from the function $$\sigma(n)$$ provide the claimed level of security, $$\lambda_{target}$$. 
+    - For each model, we perform a verification check to ensure that the values output from the function $$\sigma(n)$$ provide the claimed level of security, $$\lambda_{target}$$.
 
-These models are then used as input for Concrete, to ensure that the parameter space explored by the compiler attains the required security level. Note that we consider the `RC.BDGL16` lattice reduction cost model within the Lattice Estimator. 
+These models are then used as input for Concrete, to ensure that the parameter space explored by the compiler attains the required security level. Note that we consider the `RC.BDGL16` lattice reduction cost model within the Lattice Estimator.
 Therefore, when computing our security estimates, we use the call `LWE.estimate(params, red_cost_model = RC.BDGL16)` on the input parameter set `params`.
 
 {% hint style="warning" %}
@@ -36,7 +36,7 @@ To compare the current curves with the output of the lattice estimator, use:
 
     make compare-curves
 
-this will compare the four curves generated above against the output of the version of the lattice estimator found in the [third_party folder](https://github.com/zama-ai/concrete/tree/main/third_party). 
+this will compare the four curves generated above against the output of the version of the lattice estimator found in the [third_party folder](https://github.com/zama-ai/concrete/tree/main/third_party).
 
 To generate the associated cpp and rust code, use::
 
@@ -47,7 +47,7 @@ further advanced options can be found inside the Makefile.
 ## Example
 
 To look at the raw data gathered in step 1., we can look in the [sage-object folder](https://github.com/zama-ai/concrete/tree/main/tools/parameter-curves/sage-object). These objects can be loaded in the following way using SageMath:
-    
+
     sage: X = load("128.sobj")
 
 entries are tuples of the form: $$(n, log_2(q), log_2(\sigma), \lambda)$$. We can view individual entries via::
@@ -68,5 +68,5 @@ Here we can see the linear model parameters $$(a = -0.026599462343105267, b = 2.
 
 $$ \sigma = a * n + b = -37.85 $$
 
-This value corresponds to the logarithm of the relative error size. Using the parameter set $$(n, log(q), \sigma = 2^{64 - 37.85})$$ in the Lattice Estimator confirms a 128-bit security level. 
+This value corresponds to the logarithm of the relative error size. Using the parameter set $$(n, log(q), \sigma = 2^{64 - 37.85})$$ in the Lattice Estimator confirms a 128-bit security level.