Privacy preserving techniques in Data security?

Privacy-preserving techniques in data security are essential for protecting sensitive information while allowing for legitimate data use. These techniques ensure confidentiality, integrity, and availability without exposing sensitive details to unauthorized parties. Here are some key methods:

Encryption: Transforming data into a coded format that can only be deciphered with a key. Examples include AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman).

Homomorphic Encryption: Enables computations on encrypted data without decrypting it, making it useful for secure cloud computing and privacy-preserving machine learning.

Differential Privacy: Adds statistical noise to datasets to prevent the identification of individuals while preserving overall data utility.

Secure Multi-Party Computation (MPC): Allows multiple parties to jointly compute a function over their inputs while keeping those inputs private.

Federated Learning: A machine learning approach that trains models across multiple decentralized devices without exchanging raw data, enhancing user privacy.

Data Masking and Tokenization: Replaces sensitive data with non-sensitive equivalents, allowing for data processing without exposing real information.

Access Control and Zero Trust Architecture: Implements strict identity verification and the principle of least privilege to ensure data security.

Blockchain for Data Integrity: Uses decentralized ledgers to enhance transparency, traceability, and security in data transactions.

These techniques are widely used in healthcare, finance, and cloud computing to protect sensitive information while ensuring compliance with data protection regulations like GDPR and HIPAA.

For further reading:

Dwork, C. (2006). Differential Privacy: A Survey of Results. International Conference on Theory and Applications of Models of Computation.
Rivest, R. L., Shamir, A., & Adleman, L. (1978). A Method for Obtaining Digital Signatures and Public-Key Cryptosystems. Communications of the ACM.
Goldwasser, S., Micali, S., & Rivest, R. (1988). A Digital Signature Scheme Secure Against Adaptive Chosen-Message Attacks. SIAM Journal on Computing.

Len Leonid Mizrah

Here are some academic databases that are widely used for research:

Google Scholar: A freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines. You can access it here.

PubMed: A free search engine accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. You can find it here.

JSTOR: A digital library for academic journals, books, and primary sources. You can access it here.

IEEE Xplore: A digital library for research articles and standards in engineering and technology.

Who determines whether a migrant is assimilated, integrated, marginalized or separated?

Which regression model is suitable in determining the effect of households expenditure patterns on food security?

RNA later for the preservation of RNA in fecal samples at room temperature for one day (37°C)?

"A Markov-like Model for Patient Progression"?

Do you have good tips for seaweed tissue preservation in the field for post RNA extraction?

Whether i can preserve leaves samples in 2.5% glutaraldehyde in 0.05 M phosphate buffer, pH 7 for coming several weeks for further LM study ?

How are clinical and environmental yeast/fungal strains stored usually in mycology laboratories?

Method of collection of samples of microorganisms from environmets to labortary?

Can we preserve the protoplasts?

Plant extract sterilization???

What is your view point regarding the edible coating on the green leafy vegetables?

Which are the best datasets for Federated Learning with Differential Privacy?