In this lesson,
we will learn about Collaborative Considerations.
Collaborative considerations involve the methods
and protocols that enable secure, cooperative computing
and data sharing among multiple parties
without compromising security.
Collaborative considerations include techniques
such as homomorphic encryption
and secure multi-party computation.
Homomorphic encryption is a technique
that enables computations
to be performed on encrypted data
without ever needing to decrypt the data.
Secure multi-party computation or SMPC
is a cryptographic method
that allows several parties to work together
to compute a result using their own individual inputs,
while keeping each party's individual input
completely private.
Let's learn more about homomorphic encryption
and secure multi-party computation.
First, we have homomorphic encryption.
Homomorphic encryption is a special type of encryption
that allows computations
to be performed on data without decrypting it first.
This means an organization can process data securely
even when outsourcing to a third party cloud provider.
In this situation, the cloud provider can perform operations
on the encrypted data, but cannot read or access
the actual information because it remains encrypted.
Homomorphic encryption differs from traditional encryption
because it allows computation on the encrypted data
without needing the secret decryption key.
This keeps the data private
and confidential during processing,
making it especially valuable
for industries like finance and healthcare
where protecting sensitive information is crucial.
Homomorphic encryption works by using a public key
to encrypt data, and an algebraic system
that enables mathematical operations on the encrypted data.
The related private key is not provided to the organization
that will analyze or process the data,
meaning they can conduct data manipulation
and analysis on the data, but they can't decrypt it.
There are three main types of homomorphic encryption,
partially homomorphic encryption,
somewhat homomorphic encryption,
and fully homomorphic encryption.
Partially homomorphic encryption allows specific operations
like addition or multiplication on the data.
Somewhat homomorphic encryption
supports a limited set of operations
a certain amount of times.
Finally, fully homomorphic encryption
allows any type of computation on the encrypted data
while keeping it secure.
Homomorphic encryption relies on techniques
like private information retrieval,
secure function evaluation,
and a private function evaluation
to maintain data privacy during processing.
Private information retrieval allows a user
to retrieve data from a database
without revealing what data is accessed.
Secure function evaluation lets two parties
compute a function together
without sharing their individual inputs.
Finally, private function evaluation
goes a little bit further by allowing two parties
to compute a private function
without revealing either the inputs or the function itself.
The main challenge of homomorphic encryption
is that it is currently very slow, making it difficult
to use in modern applications.
Major companies like Microsoft and IBM
are working to improve its speed and efficiency,
aiming to make homomorphic encryption a practical tool
for secure data processing in the future.
Wow, homomorphic encryption is a complex topic.
But for the exam, remember that homomorphic encryption
allows encrypted data to be processed
without being decrypted.
The results of those computations can then be decrypted
only by the original data using their private key,
maintaining data security throughout the entire process.
Second, we have secure multi-party computation.
Secure multi-party computation or SMPC
and homomorphic encryption, both aim to enable
secure data processing without exposing private information,
but they approach the problem differently.
While homomorphic encryption allows computations
on encrypted data,
SMPC lets multiple parties jointly compute a result
using their private inputs
without revealing these inputs to each other.
Secure multi-party computation is a cryptographic technique
that enables multiple parties to jointly compute a function
with their private data
without revealing that data to each other.
This approach ensures that participants can collaborate
and reach a result without compromising the privacy
of their individual inputs.
Unlike traditional cryptography,
which focuses on protecting data from external threats,
secure multi-party computation
protects sensitive information from the very participants
involved in the computation.
So imagine a group of companies
that want to calculate the average of their sales data
without revealing their individual numbers to each other.
With secure multi-party computation,
each company can input its sales into the computation,
and the final result or the average
can be shared with everyone
without exposing any of the companies' private inputs.
This is achieved through cryptographic protocols
that ensure the data remains hidden
during the entire computation process,
allowing only the final outcome to be visible to everyone.
Within secure multi-party computation,
techniques like secure function evaluation or SFE
and private function evaluation or PFE
are used to enhance privacy.
Secure function evaluation allows multiple parties
to jointly evaluate a known function
without revealing their inputs,
ensuring that everyone only sees the final result.
Private function evaluation takes this a step further,
allowing parties to compute a private function
without revealing the function itself or the inputs used,
adding an extra layer of security.
Overall, the primary strength
of secure multi-party computation is its ability
to maintain data privacy even in collaborative settings
where multiple parties need to work together.
This makes SMPC particularly useful in scenarios
like joint data analysis, secure voting systems,
or any situation where sensitive information
must remain confidential
while still contributing to a shared result.
So remember, collaborative security techniques are essential
for enabling secure computing and data sharing
among multiple parties without compromising privacy.
These techniques include homomorphic encryption
and secure multi-party computation or SMPC,
both of which allow the data to be processed securely
without revealing sensitive information.
Homomorphic encryption does this by allowing computations
to be performed on the encrypted data without decrypting it,
keeping the data private even during processing
by third party providers.
Secure multi-party computation, on the other hand,
allows multiple parties to compute a result
using their private inputs
without exposing those inputs to one another,
ensuring that privacy is maintained
throughout the computation.
These methods are used to maintain confidentiality
and integrity, particularly in collaborative environments
where sensitive information needs to be shared
and analyzed securely.