What are these protocols trying to achieve?
```
<protocol>
Protocol 1: Basic Privacy-Preserving Nearest Neighbor Search
Process:

Client generates random masks for each dimension of their query vector.
Client computes masked query components and sends them to the server.
Server computes masked inner products with each data point and sends results back to client.
Client removes mask effects and computes distances locally.
Client identifies nearest neighbors based on computed distances.

How it solves the problem:
This protocol allows the client to find nearest neighbors in the server's public dataset without revealing the client's query. It uses random masking to hide the true query values from the server while still enabling necessary computations.
Efficiency analysis:

Computation: O(N*d) where N is the number of data points and d is the dimensionality.
Communication: O(N), as the server sends back a value for each data point.
Privacy: Good for client query, but reveals access patterns.

While this protocol preserves query privacy, its linear communication complexity makes it inefficient for large datasets.
</protocol>
<protocol>
Protocol 2: Tree-Based Privacy-Preserving Nearest Neighbor Search
Process:

Server builds a tree-based index (e.g., k-d tree) of the dataset.
Client and server perform secure traversal of the tree using cryptographic protocols.
At each node, they perform secure comparison to decide which branch to take.
Upon reaching a leaf node, server sends encrypted data points to the client.
Client decrypts data and computes distances locally to find nearest neighbor.

How it solves the problem:
This protocol reduces communication complexity by using a hierarchical structure. It preserves privacy through secure comparisons and encrypted data transfer.
Efficiency analysis:

Computation: O(log N) secure comparisons, plus local distance computations.
Communication: O(log N), as it's proportional to the depth of the tree.
Privacy: Improved over basic protocol, but may leak some information through access patterns unless additional measures (like PIR or ORAM) are used.

This protocol significantly improves communication efficiency compared to the basic approach, making it more suitable for larger datasets.
</protocol>
<protocol>
Protocol 3: Locality-Sensitive Hashing (LSH) Based Privacy-Preserving Nearest Neighbor Search
Process:

Server constructs multiple LSH hash tables from the dataset.
Client and server perform secure computation to hash the query and identify corresponding buckets.
Server sends data from relevant buckets to the client.
Client computes distances locally to find approximate nearest neighbor.

How it solves the problem:
This protocol uses LSH to reduce the search space, allowing for sublinear communication. It preserves privacy through secure bucket retrieval.
Efficiency analysis:

Computation: Depends on LSH parameters, but generally sublinear.
Communication: O(N^(1/c)) where c > 1, sublinear in dataset size.
Privacy: Good query privacy, but may leak some information about query location in the hash space.

This protocol offers a trade-off between accuracy and efficiency, providing approximate results with sublinear communication.
</protocol>
<protocol>
Protocol 4: Privacy-Preserving Hierarchical Navigable Small World (HNSW) Graph
Process:

Server builds HNSW index on the dataset.
Client uses Private Information Retrieval (PIR) to securely fetch the entry point.
Client performs oblivious graph traversal:
a. Use PIR to retrieve neighbor lists of current node.
b. Compute distances locally.
c. Select next node to visit.
d. Repeat until reaching bottom layer or no closer neighbor is found.
Client identifies the approximate nearest neighbor.

How it solves the problem:
This protocol leverages the efficiency of HNSW while using PIR to hide access patterns. It preserves query privacy and prevents the server from learning the traversal path.
Efficiency analysis:

Computation: O(log N) or O(sqrt(N)) PIR queries, plus local distance computations.
Communication: Sublinear, O(log N) or O(sqrt(N)), depending on HNSW parameters.
Privacy: Strong query privacy and hidden access patterns due to PIR.

This protocol offers a good balance between efficiency and privacy, achieving sublinear communication while strongly protecting the client's query and access patterns.
</protocol>
```