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+**/__pycache__
+**/.ipynb_checkpoints
+**/local_tests**
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diff --git a/NearestNeighbors/README.MD b/NearestNeighbors/README.MD
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+# K Nearest Neighbors
+## Introduction
+
+The use of maps to find nearest point of interest (e.g. closest restaurants) is common. However, using such features may reveals sensitive personal information. In this tutorial, we demonstrate how to use Zama's homomorphic encryption to find the K nearest restaurants to a given input while preserving the localisation's privacy.
+
+In this scenario, the server does not learn anything about a client's position at anytime as it sees only encrypted data.
+
+## Achieving Privacy
+In our application demo:
+
+ 1. The client owns its position. This information is never shared in clear with the server.
+ 1. The server owns map data. It only shares with the client the coordinates of the K closest restaurants to its position.
+
+To achieve this, FHE is used to implement swap sort. The server receives the encrypted coordinates and calculates the distance for each restaurant on the list. The server then performs a partial sort using swaps (an equivalent to [bubble sort](https://en.wikipedia.org/wiki/Bubble_sort)). It retrieves the encrypted coordinates of the K nearest restaurants and sends them to the client. The client decrypts the results.
+
+## Demo App
+A demo streamlit app is available on [Quadratic's HuggingFace](https://huggingface.co/spaces/Quadratic-Labs/PrivateNearestNeighbors-FHE):
+
+It can also be deployed locally in the usual way:
+
+ 1. Create a python virtual environnement and activate it.
+ 1. Install dependencies from requirements.txt: pip install -r requirements.txt
+ 1. Launch the app: streamlit run app.py
+
+The app depends also on files found in data/ folder:
+
+ 1. circuit.zip : use generate_circuit.py to recompile the circuit if necessary (on new network data).
+ 1. key.zip : cached keys. If not present, they will be generated.
+ 1. restaurants.geojson: map data extracted from OSM (overpass-turbo API). See knn.ipynb for more information.
+
+## Data Extraction
+
+The data used as example in this notebook are extracted from [overpass-turbo](https://overpass-turbo.eu) using the following query :
+
+```
+ // gather results
+ nwr["amenity"="restaurant"](48.874256063816,2.3389828205109,48.876909238658,2.3433548212051);
+
+ // print results
+ out;
+```
+
+This query returns a list of restaurants in a given area
+
+
+## Explanation of the swap sort
+
+
+The swap sort is implemented using three TLUs :
+```
+ relu = fhe.univariate(lambda x: x if x > 0 else 0)
+ is_positive = fhe.univariate(lambda x: 1 if x > 0 else 0)
+ odd_halving = fhe.univariate(lambda x: (x - 1) // 2 if x % 2 else 0)
+```
+
+The first returns x if x is strictly positive and zero otherwise.
+The second returns one if x is positive and zero otherwise.
+The last returns half the value minus one for odd numbers, and zero otherwise.
+
+
+The combination of the three functions is used to perform the swap. Here a snippet of the swap function:
+
+```
+def swap(this_idx, this_dist, that_idx, that_dist):
+ """
+ Swaps this and that if this > that.
+ We must pass both the index and the distance for both this and that.
+
+ Returns:
+ idxmin, min, idxmax, max of this and that based on distance
+ """
+ diff = this_dist - that_dist
+ idx = odd_halving(2 * (this_idx - that_idx) + is_positive(diff))
+ dist = relu(diff)
+
+ idx_min = this_idx - idx
+ idx_max = that_idx + idx
+ dist_min = this_dist - dist
+ dist_max = that_dist + dist
+ return fhe.array([idx_min, dist_min, idx_max, dist_max])
+```
+
+To decide weither or not to swap two values with their respective indices, we start by calculating the difference.
+
+```
+diff = this_dist - that_dist
+```
+
+Then, on the basis of this difference, we calculate 'idx', the index change to be made on the original indices.
+
+
+```
+idx = odd_halving(2 * (this_idx - that_idx) + is_positive(diff))
+```
+
+If the difference is positive (i.e. this > that), the input to odd_halving is always odd (2 * (this_idx - that_idx) is even).
+The result of odd_halving is then (this_idx - that_idx), which is the difference to be applied to the original indices. Otherwise, the result is zero and the indices remain unchanged.
+
+
+The values are swapped (or not) depending on the diff value. We use a relu to achieve this. If the difference is positive, the values should be swaped, otherwise the relu returns zero and the values remain unchanged.
+
+
+The code extract below illustrates how the swap function works.
+
+
+```
+if this > that :
+ diff = this_dist - that_dist > 0
+ idx = odd_halving(2 * (this_idx - that_idx) + is_positive(diff)) = odd_halving(2 * (this_idx - that_idx) + 1) = this_idx - that_idx
+ dist = relu(diff) = diff
+
+ idx_min = this_idx - idx = this_idx - (this_idx - that_idx) = that_idx
+ idx_max = that_idx + idx = that_idx + (this_idx - that_idx) = this_idx
+ dist_min = this_dist - dist = this_dist - diff = this_dist - (this_dist - that_dist) = that_dist
+ dist_max = that_dist + dist = that_dist + diff = that_dist + (this_dist - that_dist) = this_dist
+
+else :
+ diff = this_dist - that_dist < 0
+ idx = odd_halving(2 * (this_idx - that_idx) + is_positive(diff)) = odd_halving(2 * (this_idx - that_idx) + 0) = 0
+ dist = relu(diff) = 0
+
+ idx_min = this_idx - idx = this_idx
+ idx_max = that_idx + idx = that_idx
+ dist_min = this_dist - dist = this_dist
+ dist_max = that_dist + dist = that_dist
+```
+
+## Size of the map
+The list of restaurants is presently confined to a predetermined area. We select seven restaurants and adjust the number of neighbors, K, to two (both parameters can be updated in config.py). With the current configuration on the Hugging Face server, the expected running times are up to 15 seconds for key generation and up to 4 minutes for the run. Increasing the number of displayed restaurants or the number of neighbors will significantly increase the execution time. Please refer to the figure below for different settings.
+
+
+
+One potential optimization is using [heap sort](https://en.wikipedia.org/wiki/Heapsort) instead of swap sort. This change will decrease the number of comparisons and, presumably, reduce the execution time.
\ No newline at end of file
diff --git a/NearestNeighbors/app.py b/NearestNeighbors/app.py
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+import network
+from shapely.geometry import Point
+import streamlit as st
+import config
+from utils import (
+ set_up_server,
+ set_up_client,
+ display_encrypted,
+ transform_point,
+ process_result,
+ display_map,
+ init_session,
+ add_to_server_side,
+ add_to_client_side,
+ display_client_side,
+ display_server_side,
+ restart_session,
+)
+from concrete import fhe
+
+server = set_up_server()
+client = set_up_client(server.client_specs.serialize())
+restaurants, point_coordinates = network.get()
+
+c1, c2, c3 = init_session()
+
+with c1:
+ display_client_side()
+with c2:
+ st_map = display_map(restaurants)
+with c3:
+ display_server_side()
+
+
+# keys generation view
+if "evaluation_key" not in st.session_state:
+ with c1:
+ if st.button("Generate keys"):
+ with st.spinner("Generating keys"):
+ client.keys.load_if_exists_generate_and_save_otherwise(
+ config.keys_filepath
+ )
+ st.session_state["evaluation_key"] = client.evaluation_keys.serialize()
+ add_to_client_side(
+ "Encryption/decryption keys and evaluation keys are generated."
+ )
+ add_to_client_side("The evaluation key is sent to the server.")
+ add_to_server_side(
+ f"Evaluation key: {display_encrypted(st.session_state['evaluation_key'])}"
+ )
+ st.rerun()
+
+else:
+ if "position" not in st.session_state:
+ with c1:
+ st.write("Select your starting position on the st_map")
+
+ if st_map.get("last_clicked"):
+ position = Point(
+ st_map.get("last_clicked")["lng"], st_map.get("last_clicked")["lat"]
+ )
+ add_to_client_side(
+ f"Selected starting point is: ({position.x}, {position.y})"
+ )
+ st.session_state["position"] = position
+ st.rerun()
+
+ if "position" in st.session_state and "encrypted_position" not in st.session_state:
+ position = st.session_state["position"]
+ with c1:
+ if st.button("Encrypt and send inputs"):
+ with st.spinner("Encrypting inputs"):
+ client.keys.load(config.keys_filepath)
+
+ x, y = transform_point(position.x, position.y)
+ encrypted_x, encrypted_y = client.encrypt(x, y)
+ encrypted_position = (
+ encrypted_x.serialize(),
+ encrypted_y.serialize(),
+ )
+ add_to_server_side(
+ f"Received starting point: ({display_encrypted(encrypted_position[0])},{display_encrypted(encrypted_position[1])})"
+ )
+ st.session_state["encrypted_position"] = encrypted_position
+ st.rerun()
+
+ if (
+ "encrypted_position" in st.session_state
+ and "encrypted_results" not in st.session_state
+ ):
+ with c3:
+ encrypted_x = st.session_state["encrypted_position"][0]
+ encrypted_y = st.session_state["encrypted_position"][1]
+
+ if st.button(f"Find {config.number_of_neighbors} nearest restaurants"):
+ with st.spinner("Computing"):
+ st.write("Please wait, it may take few minutes.")
+ deserialized_x = fhe.Value.deserialize(encrypted_x)
+ deserialized_y = fhe.Value.deserialize(encrypted_y)
+ deserialized_evaluation_keys = fhe.EvaluationKeys.deserialize(
+ st.session_state["evaluation_key"]
+ )
+ client.keys.load_if_exists_generate_and_save_otherwise(
+ config.keys_filepath
+ )
+ res = server.run(
+ deserialized_x,
+ deserialized_y,
+ evaluation_keys=deserialized_evaluation_keys,
+ )
+ ser_res = fhe.Value.serialize(res)
+ add_to_server_side(
+ f"The encrypted result {display_encrypted(ser_res)} is sent to the client."
+ )
+ add_to_client_side(
+ f"The received encrypted result is: {display_encrypted(ser_res)}."
+ )
+ st.session_state["encrypted_results"] = ser_res
+ st.rerun()
+
+ if (
+ "encrypted_results" in st.session_state
+ and "decrypted_result" not in st.session_state
+ ):
+ res = st.session_state["encrypted_results"]
+ with c1:
+ if st.button("Decrypt result"):
+ with st.spinner("Computing"):
+ client.keys.load_if_exists_generate_and_save_otherwise(
+ config.keys_filepath
+ )
+ deser_res = fhe.Value.deserialize(res)
+ decrypted_result = client.decrypt(deser_res)
+ process_result(restaurants, decrypted_result)
+ st.session_state["decrypted_result"] = decrypted_result
+ st.rerun()
+
+ if "decrypted_result" in st.session_state:
+ with c1:
+ restart_session()
diff --git a/NearestNeighbors/comparison/KnnConcrete.ipynb b/NearestNeighbors/comparison/KnnConcrete.ipynb
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@@ -0,0 +1,208 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/riad/envs/zama/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
KNeighborsClassifier(n_bits=6)
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