Cryptographic techniques for securing big data analytics

Cryptographic techniques for securing

big data analytics

Introduction

With the exponential growth of data being generated every day, data

analytics has become a crucial component for extracting useful insights.

However, as more and more personal and sensitive data is collected and

analyzed, security and privacy concerns have also increased exponentially.

Traditional encryption techniques alone cannot adequately address the

challenges of securing large volumes of data during transmission and

analytics is homomorphic encryption. Homomorphic encryption allows

specific types of computations to be performed directly on encrypted data

without having to decrypt it first. This means analytics can be run on

encrypted data and the results can still be encrypted. The two main types of

homomorphic encryption schemes are:

- Partially Homomorphic Encryption (PHE): Allows only a limited set of

operations like addition or multiplication to be performed on encrypted

data. An example is the Pallier cryptosystem which supports addition

but not multiplication.

- Fully Homomorphic Encryption (FHE): Supports both addition and

FHE. Researchers have since proposed more efficient FHE schemes based on

ring learning with errors problem (R-LWE) and module learning with errors

problem (MLWE). The latest FHE schemes like the BFV cryptosystem support

arbitrary computations on encrypted data with reasonable computation

times suitable for applications.

FHE enables outsourcing the storage and processing of sensitive data to third

parties like cloud servers while keeping the data encrypted throughout. For

example, a hospital can encrypt patient records and outsource the storage to

a cloud. Authorized doctors and researchers can generate statistics and run

medical studies on the encrypted records without decrypting individual

big data analytics involving sensitive or private data from multiple sources.

An Important application of MPC is private set intersection, where two or

more parties want to compute the intersection of their private datasets

without revealing anything else about the datasets. For example, companies

can compare customer lists to identify overlapping customers for targeted

marketing without sharing their full customer databases.

distributed datasets across parties in a privacy-preserving manner.

circuits, GMW and SPDZ protocols. The computation and communication

costs have significantly reduced over the years due to optimizations. MPC is

being adopted in practice for collaborative data analytics in banking,

healthcare and other sectors involving sensitive multi-party data.

Differential Privacy

Differential privacy is a framework for privacy-preserving data analytics that

aims to limit the risk of identifying individuals from aggregate query results

- The Laplace mechanism adds Laplacian noise proportional to the

sensitivity of a query to its output before releasing the result. It is

simple and easy to implement.

- The exponential mechanism selects an output based on a quality

function and adds noise via an exponential distribution. It ensures

privacy for computational queries.

Differential privacy is widely adopted in big data analytics and machine

their mobile analytics to collect usage data from users without identifying

individuals. It enables big data-driven research in sectors like healthcare and

social sciences while preserving privacy.

Blockchain for Privacy-Preserving Analytics

Blockchain, popularly known as the technology behind Bitcoin, can also

enable privacy-preserving big data sharing and analytics. Blockchain

maintains an immutable, auditable log of transactions in a distributed

- Decentralization: Data is stored across a network of nodes instead of a

techniques like ring signatures or zk-SNARKs so the actual parties

involved remain private even on the public blockchain.

- Provenance & Immutability: Once recorded, data in the blockchain

cannot be altered or erased, ensuring integrity and establishing a clear

history of access.

datasets anonymously for analytics under privacy restrictions via smart

Real-world Cryptography Applications

There are several real-world implementations of cryptographic techniques for

securing sensitive big data analytics:

- Encrypted genome analysis – Genomic data company Anthropic uses

homomorphic encryption to allow training ML models on encrypted

genomes from DNA sequencing companies without decrypting

- Financial data collaboration – Banks in Japan used secure computation

protocols like MPC to jointly analyze trends in consumer credit data

by Apple/Google implemented differential privacy during Bluetooth-

based contact logging on smartphones to trace viral spread privately.

- Cryptographic marketplaces – Enigma allows companies to monetize

sensitive customer datasets on its blockchain-based platform through

differentially private analytics smart contracts.

- Smart city applications – Differentially private aggregation of transit

Conclusion

As data analytics drives new opportunities as well as privacy challenges,

cryptographic techniques provide a rigorous foundation for securing sensitive

big data. Homomorphic encryption, secure computation, differential privacy

and blockchain backed by rigorous security definitions enable practical yet

privacy-aware data-driven applications at scale. Going forward, further

analytics has become a crucial component for extracting useful insights.

However, as more and more personal and sensitive data is collected and

analyzed, security and privacy concerns have also increased exponentially.

Traditional encryption techniques alone cannot adequately address the

challenges of securing large volumes of data during transmission and

analytics is homomorphic encryption. Homomorphic encryption allows

specific types of computations to be performed directly on encrypted data

without having to decrypt it first. This means analytics can be run on

encrypted data and the results can still be encrypted. The two main types of

homomorphic encryption schemes are:

- Partially Homomorphic Encryption (PHE): Allows only a limited set of

operations like addition or multiplication to be performed on encrypted

data. An example is the Pallier cryptosystem which supports addition

but not multiplication.

- Fully Homomorphic Encryption (FHE): Supports both addition and

multiplication operations on encrypted data. This allows any arbitrary

ring learning with errors problem (R-LWE) and module learning with errors

problem (MLWE). The latest FHE schemes like the BFV cryptosystem support

arbitrary computations on encrypted data with reasonable computation

times suitable for applications.

FHE enables outsourcing the storage and processing of sensitive data to third

parties like cloud servers while keeping the data encrypted throughout. For

example, a hospital can encrypt patient records and outsource the storage to

a cloud. Authorized doctors and researchers can generate statistics and run

medical studies on the encrypted records without decrypting individual

records first. FHE provides privacy-preserving data analytics as the server

can compare customer lists to identify overlapping customers for targeted

Another major application is secure auctions, where bidders can submit

encrypted bids and learn only the auction outcome without revealing their

private bids. MPC even enables training machine learning models on

distributed datasets across parties in a privacy-preserving manner.

There are different MPC protocols based on secret sharing, garbled circuits

and other cryptographic techniques. Popular protocols include Yao’s garbled

circuits, GMW and SPDZ protocols. The computation and communication

costs have significantly reduced over the years due to optimizations. MPC is

being adopted in practice for collaborative data analytics in banking,

healthcare and other sectors involving sensitive multi-party data.

parties like cloud servers while keeping the data encrypted throughout. For

medical studies on the encrypted records without decrypting individual

records first. FHE provides privacy-preserving data analytics as the server

never sees the actual raw data.

the differentially private dataset does not meaningfully increase their chance

of determining whether any individual’s data is included in the results.

Some common differential privacy techniques used in big data analytics are:

- Decentralization: Data is stored across a network of nodes instead of a

single centralized repository, limiting control and access by any single

entity.

datasets anonymously for analytics under privacy restrictions via smart

contracts. Overall, blockchain provides a promising infrastructure for privacy-

aware distributed learning via its inherent transparency and security

properties when combined with cryptography.

protocols like MPC to jointly analyze trends in consumer credit data

while keeping individual records private.

specific types of computations to be performed directly on encrypted data

without having to decrypt it first. This means analytics can be run on

encrypted data and the results can still be encrypted. The two main types of

homomorphic encryption schemes are:

but not multiplication.

computation to be performed on encrypted data. However, FHE

schemes proposed earlier were not practical due to very high

computational costs.

scheme based on ideal lattices. This sparked intense research interest in

FHE. Researchers have since proposed more efficient FHE schemes based on

ring learning with errors problem (R-LWE) and module learning with errors

problem (MLWE). The latest FHE schemes like the BFV cryptosystem support

arbitrary computations on encrypted data with reasonable computation

FHE enables outsourcing the storage and processing of sensitive data to third

parties like cloud servers while keeping the data encrypted throughout. For

example, a hospital can encrypt patient records and outsource the storage to

a cloud. Authorized doctors and researchers can generate statistics and run

medical studies on the encrypted records without decrypting individual

records first. FHE provides privacy-preserving data analytics as the server

never sees the actual raw data.

Secure Multi-party Computation

Secure multi-party computation (MPC) allows multiple parties to jointly

compute a function over their inputs while keeping these inputs private. No

party learns anything except their predefined output. MPC is well-suited for

big data analytics involving sensitive or private data from multiple sources.

can compare customer lists to identify overlapping customers for targeted

marketing without sharing their full customer databases.

Another major application is secure auctions, where bidders can submit

There are different MPC protocols based on secret sharing, garbled circuits

and other cryptographic techniques. Popular protocols include Yao’s garbled

circuits, GMW and SPDZ protocols. The computation and communication

costs have significantly reduced over the years due to optimizations. MPC is

being adopted in practice for collaborative data analytics in banking,

over a statistical database. It achieves this by introducing “noise” to query

outputs in a mathematically provable way. Differential privacy provides a

privacy guarantee – even if an attacker has outside information, accessing

the differentially private dataset does not meaningfully increase their chance

of determining whether any individual’s data is included in the results.

Some common differential privacy techniques used in big data analytics are:

- The exponential mechanism selects an output based on a quality

- Privacy accounting techniques like the advanced composition theorem

allow differential privacy to extend over multiple analyses on the same

dataset based on the total “privacy loss”.

Differential privacy is widely adopted in big data analytics and machine

learning where models are trained on large datasets while preserving

individual privacy. For example, Apple and Google use differential privacy in

their mobile analytics to collect usage data from users without identifying

maintains an immutable, auditable log of transactions in a distributed

database called a ledger maintained across a peer-to-peer network. Key

features helpful for sensitive data analytics include:

- Decentralization: Data is stored across a network of nodes instead of a

single centralized repository, limiting control and access by any single

entity.

history of access.

Researchers have proposed frameworks to deploy differentially private

analytics models directly on blockchains. Others utilize blockchain to create

marketplaces where data owners can monetize or share their private

contracts. Overall, blockchain provides a promising infrastructure for privacy-

aware distributed learning via its inherent transparency and security

properties when combined with cryptography.

There are several real-world implementations of cryptographic techniques for

genomes from DNA sequencing companies without decrypting

individual records.

- Financial data collaboration – Banks in Japan used secure computation

protocols like MPC to jointly analyze trends in consumer credit data

while keeping individual records private.

- Cryptographic marketplaces – Enigma allows companies to monetize

- Healthcare collaboration – Massive Health enables hospitals to securely

run federated analytics on encrypted HER records via homomorphic

encryption to accelerate medical research.

- Smart city applications – Differentially private aggregation of transit

smart card logs and traffic sensor data helps improve city

infrastructure planning without privacy risks.

privacy-aware data-driven applications at scale. Going forward, further

optimizations, standardized techniques and increased adoption will help

unlock the benefits of big data analytics while safeguarding privacy.

Cryptography will play a key role in balancing utility and ethics in the big

data era.

With the exponential growth of data being generated every day, data

However, as more and more personal and sensitive data is collected and

analyzed, security and privacy concerns have also increased exponentially.

schemes proposed earlier were not practical due to very high

computational costs.

ring learning with errors problem (R-LWE) and module learning with errors

problem (MLWE). The latest FHE schemes like the BFV cryptosystem support

arbitrary computations on encrypted data with reasonable computation

times suitable for applications.

FHE enables outsourcing the storage and processing of sensitive data to third

Secure Multi-party Computation

Secure multi-party computation (MPC) allows multiple parties to jointly

compute a function over their inputs while keeping these inputs private. No

party learns anything except their predefined output. MPC is well-suited for

big data analytics involving sensitive or private data from multiple sources.

An Important application of MPC is private set intersection, where two or

more parties want to compute the intersection of their private datasets

without revealing anything else about the datasets. For example, companies

encrypted bids and learn only the auction outcome without revealing their

There are different MPC protocols based on secret sharing, garbled circuits

and other cryptographic techniques. Popular protocols include Yao’s garbled

circuits, GMW and SPDZ protocols. The computation and communication

costs have significantly reduced over the years due to optimizations. MPC is

healthcare and other sectors involving sensitive multi-party data.

Differential Privacy

aims to limit the risk of identifying individuals from aggregate query results

over a statistical database. It achieves this by introducing “noise” to query

Some common differential privacy techniques used in big data analytics are:

- The Laplace mechanism adds Laplacian noise proportional to the

sensitivity of a query to its output before releasing the result. It is

simple and easy to implement.

dataset based on the total “privacy loss”.

Differential privacy is widely adopted in big data analytics and machine

learning where models are trained on large datasets while preserving

individual privacy. For example, Apple and Google use differential privacy in

individuals. It enables big data-driven research in sectors like healthcare and

social sciences while preserving privacy.

Blockchain for Privacy-Preserving Analytics

Blockchain, popularly known as the technology behind Bitcoin, can also

enable privacy-preserving big data sharing and analytics. Blockchain

- Decentralization: Data is stored across a network of nodes instead of a

single centralized repository, limiting control and access by any single

entity.

- Anonymity: Data can be anonymized on ingestion via cryptographic

techniques like ring signatures or zk-SNARKs so the actual parties

involved remain private even on the public blockchain.

Researchers have proposed frameworks to deploy differentially private

datasets anonymously for analytics under privacy restrictions via smart

contracts. Overall, blockchain provides a promising infrastructure for privacy-

aware distributed learning via its inherent transparency and security

properties when combined with cryptography.

Real-world Cryptography Applications

There are several real-world implementations of cryptographic techniques for

securing sensitive big data analytics:

homomorphic encryption to allow training ML models on encrypted

genomes from DNA sequencing companies without decrypting

while keeping individual records private.

- COVID-19 contact tracing – Private Automated Contact Tracing (PACT)

by Apple/Google implemented differential privacy during Bluetooth-

based contact logging on smartphones to trace viral spread privately.

encryption to accelerate medical research.

- Smart city applications – Differentially private aggregation of transit

smart card logs and traffic sensor data helps improve city

infrastructure planning without privacy risks.

Conclusion

big data. Homomorphic encryption, secure computation, differential privacy

and blockchain backed by rigorous security definitions enable practical yet