From 15a2e70012ebd0e104d9992a61e53582b71c2f36 Mon Sep 17 00:00:00 2001 From: cybermazi Date: Tue, 18 Jul 2023 12:23:34 +0100 Subject: [PATCH 1/2] blog_post1 Signed-off-by: cybermazi --- .idea/workspace.xml | 50 ++++++++++++++++++++++++ blog/2021-08-26-welcome/index.md | 1 - blog/authors.yml | 9 +++++ blog/intro-to-privacy-tech/blog.md | 63 ++++++++++++++++++++++++++++++ 4 files changed, 122 insertions(+), 1 deletion(-) create mode 100644 .idea/workspace.xml create mode 100644 blog/intro-to-privacy-tech/blog.md diff --git a/.idea/workspace.xml b/.idea/workspace.xml new file mode 100644 index 00000000..0e52c119 --- /dev/null +++ b/.idea/workspace.xml @@ -0,0 +1,50 @@ + + + + + + + + + + + + + + + + + + + { + "keyToString": { + "RunOnceActivity.OpenProjectViewOnStart": "true", + "RunOnceActivity.ShowReadmeOnStart": "true" + } +} + + + + + + + + + + 1689054912092 + + + + \ No newline at end of file diff --git a/blog/2021-08-26-welcome/index.md b/blog/2021-08-26-welcome/index.md index 61ed656c..f283221a 100644 --- a/blog/2021-08-26-welcome/index.md +++ b/blog/2021-08-26-welcome/index.md @@ -5,4 +5,3 @@ authors: [sourav] tags: [announcements, federated learning, differential privacy] --- -We are updating the blog page, if you want to write a blog or contribute contact info@openprivacytech.org \ No newline at end of file diff --git a/blog/authors.yml b/blog/authors.yml index 07cb5ded..170b34bb 100644 --- a/blog/authors.yml +++ b/blog/authors.yml @@ -3,3 +3,12 @@ sourav: title: Core Team url: https://github.com/souravcipher image_url: https://github.com/souravcipher.png + + +caleb: + name: Akalezi Caleb + title: Technical writer at OpenPrivacyTech + url: https://github.com/cybermazi + image_url: https://github.com/cybermazi.png + twitter: calebsgram + email: takalezi6@gmail.com \ No newline at end of file diff --git a/blog/intro-to-privacy-tech/blog.md b/blog/intro-to-privacy-tech/blog.md new file mode 100644 index 00000000..d9b339b9 --- /dev/null +++ b/blog/intro-to-privacy-tech/blog.md @@ -0,0 +1,63 @@ +--- +title: Technical Overview of Privacy Technology +authors: [caleb] +tags: [announcements, federated learning, differential privacy, blog] +--- +**Introduction** + +In today's modern world, the privacy of our data has become a critical concern. Due to the increase in usage of technology and the rise of data-driven services, there's a significant need for privacy technology to ensure the confidentiality and safety of our sensitive data. Several privacy technologies have been developed in recent years that aim to protect the privacy of user data. This technical overview aims to provide a detailed insight into some of the most prevalent privacy technologies. + +**Differential Privacy** + +**Differential privacy (DP)** is a mathematical framework for ensuring the privacy of individuals in datasets. It can provide a strong guarantee of privacy by allowing data to be analyzed without revealing sensitive information about any individual in the dataset. +The idea behind differential privacy is that if the effect of making an arbitrary single substitution in the database is small enough, the query result cannot be used to infer much about any single individual, and therefore provides privacy. + +**DP** ensures that the results of any analysis of a dataset are statistically indistinguishable from the results of an analysis of a dataset that is missing a single individual's data. This means that it is impossible to identify individual records from the results of the analysis. +Here's how differential privacy works: +* Add noise to the data. Differential privacy works by adding noise to data in a way that preserves the overall statistical properties of the data, while making it difficult to identify individual records. +* The noise is calibrated to ensure privacy. The amount of noise that is added to the data is calibrated to ensure that it is impossible to identify individual records with high confidence. +* The noisy data can then be analyzed. The noisy data can then be analyzed to extract useful insights or perform data analysis. +* The results of the analysis are statistically indistinguishable from the results of an analysis of a dataset that is missing a single individual's data. This means that it is impossible to identify individual records from the results of the analysis. + +Differential privacy is a powerful tool for protecting the privacy of individuals in datasets. It is relatively easy to implement and can be used in a variety of settings. + +To learn more about Differential Privacy, [check here](/resources/differential-privacy). + +**Federated Learning** + +**Federated learning** is a decentralized approach to machine learning that allows multiple parties to collaboratively train a shared model while keeping their data local. It enables privacy-preserving machine learning by allowing data to remain on local devices or servers, avoiding the need to transfer sensitive information to a centralized location. + +Federated learning operates through the following steps: +* Model initialization: The shared model is initially created and distributed to the participating devices or servers. +* Local training: Each device or server performs training using its local data while keeping the data private and secure. +* Model aggregation: The locally trained models are sent back to a central server or aggregator, which combines the models' updates to generate a global model. +* Model update distribution: The updated global model is redistributed to the participating devices or servers. +* Iterative process: Steps 2-4 are repeated iteratively, allowing the model to improve with contributions from multiple participants' data without compromising privacy. + +**Federated learning** offers several advantages, including: +1. Privacy preservation: Participant data remains local, reducing the risk of privacy breaches or data leakage. +2. Data ownership: Participants retain control over their data and can decide whether to contribute or opt-out. +3. Reduced data transfer: Only model updates are shared, minimizing the amount of data sent across the network. + +To learn more about Federated Learning, [check here](/resources/federated-learning). + +**Homomorphic encryption** + +This is a groundbreaking privacy technology that allows computations to be performed on encrypted data without decrypting it. It provides a way to securely process sensitive information while keeping it encrypted, ensuring privacy and confidentiality. With homomorphic encryption, data can be processed by third-party service providers or cloud platforms without the need to expose the decrypted data to them. + +Here's how homomorphic encryption works: +1. Data encryption: The data is encrypted using a special encryption scheme that supports homomorphic operations. This encryption ensures that the data remains confidential and protected. +2. Computations on encrypted data: The encrypted data can undergo various computations and operations, such as addition, multiplication, or comparison, without decrypting it. The homomorphic encryption scheme allows these operations to be performed on the encrypted data directly. +3. Result decryption: Once the desired computations are completed, the final result is decrypted using a secret key known only to the data owner. The decrypted result reveals the outcome of the computations while keeping the original data secure. + +Homomorphic encryption enables secure data processing in scenarios where privacy is of utmost importance. It has applications in various domains, including healthcare, finance, and data analysis. +Some advantages of homomorphic encryption include: +* Confidentiality: Homomorphic encryption ensures that sensitive data remains encrypted throughout the computation process, protecting it from unauthorized access. +* Outsourcing computations: Individuals or organizations can securely outsource computations to third-party providers without revealing the underlying data. + +Homomorphic encryption is an exciting area of research with the potential to revolutionize data processing while maintaining privacy and security. It provides a practical solution for scenarios where secure computations on sensitive data are required. +To learn more about Homomorphic Encryption, [check here](/resources/homomorphic-encryption) + +**Conclusion** + +This article discusses few examples of the many privacy technologies available today. There are many more technologies and techniques that can be used to protect user data. By leveraging the right privacy technology, companies and organizations can continue to provide data-driven services while respecting users' privacy rights. From c13d4d477f43f9e04a16e4498ea59764c3adebd3 Mon Sep 17 00:00:00 2001 From: cybermazi Date: Wed, 2 Aug 2023 15:51:31 +0100 Subject: [PATCH 2/2] blog_update_1.2. Signed-off-by: cybermazi --- blog/intro-to-privacy-tech/blog.md | 25 +++++++++++++------------ 1 file changed, 13 insertions(+), 12 deletions(-) diff --git a/blog/intro-to-privacy-tech/blog.md b/blog/intro-to-privacy-tech/blog.md index d9b339b9..d1810ace 100644 --- a/blog/intro-to-privacy-tech/blog.md +++ b/blog/intro-to-privacy-tech/blog.md @@ -9,25 +9,27 @@ In today's modern world, the privacy of our data has become a critical concern. **Differential Privacy** -**Differential privacy (DP)** is a mathematical framework for ensuring the privacy of individuals in datasets. It can provide a strong guarantee of privacy by allowing data to be analyzed without revealing sensitive information about any individual in the dataset. -The idea behind differential privacy is that if the effect of making an arbitrary single substitution in the database is small enough, the query result cannot be used to infer much about any single individual, and therefore provides privacy. +**Differential privacy (DP)** is a framework to analyze data without revealing information about individual participants. +It works by adding noise to the dataset , allowing data to be analyzed without revealing sensitive information about any individual, which makes it difficult to link any particular individual to their data. +The concept of differential privacy is that if the result of a single random substitution in the database is negligible, the query result cannot be used to gather significant information about any single individual.This ensures that participants' privacy is protected, even if the data is shared with others. + **DP** ensures that the results of any analysis of a dataset are statistically indistinguishable from the results of an analysis of a dataset that is missing a single individual's data. This means that it is impossible to identify individual records from the results of the analysis. Here's how differential privacy works: -* Add noise to the data. Differential privacy works by adding noise to data in a way that preserves the overall statistical properties of the data, while making it difficult to identify individual records. +* Add noise to the data. It works by adding noise to data, a common data encryption method, making individual records difficult to identify while preserving their overall statistical properties. * The noise is calibrated to ensure privacy. The amount of noise that is added to the data is calibrated to ensure that it is impossible to identify individual records with high confidence. * The noisy data can then be analyzed. The noisy data can then be analyzed to extract useful insights or perform data analysis. * The results of the analysis are statistically indistinguishable from the results of an analysis of a dataset that is missing a single individual's data. This means that it is impossible to identify individual records from the results of the analysis. -Differential privacy is a powerful tool for protecting the privacy of individuals in datasets. It is relatively easy to implement and can be used in a variety of settings. +Differential privacy is an effective means of protecting individual privacy within a dataset. It is relatively easy to implement and can be used in a variety of settings. To learn more about Differential Privacy, [check here](/resources/differential-privacy). **Federated Learning** -**Federated learning** is a decentralized approach to machine learning that allows multiple parties to collaboratively train a shared model while keeping their data local. It enables privacy-preserving machine learning by allowing data to remain on local devices or servers, avoiding the need to transfer sensitive information to a centralized location. +**Federated learning** is a distributed method of machine learning that enables multiple entities to collectively train a common model while preserving the privacy of their individual data. It enables privacy-preserving machine learning by allowing data to remain on local devices or servers, avoiding the need to transfer sensitive information to a centralized location. -Federated learning operates through the following steps: +Here's how Federated learning works : * Model initialization: The shared model is initially created and distributed to the participating devices or servers. * Local training: Each device or server performs training using its local data while keeping the data private and secure. * Model aggregation: The locally trained models are sent back to a central server or aggregator, which combines the models' updates to generate a global model. @@ -43,19 +45,18 @@ To learn more about Federated Learning, [check here](/resources/federated-learni **Homomorphic encryption** -This is a groundbreaking privacy technology that allows computations to be performed on encrypted data without decrypting it. It provides a way to securely process sensitive information while keeping it encrypted, ensuring privacy and confidentiality. With homomorphic encryption, data can be processed by third-party service providers or cloud platforms without the need to expose the decrypted data to them. - +This innovative privacy technology enables computations to be conducted on encrypted data without the need for decryption. It provides a way to securely process sensitive information while keeping it encrypted, ensuring privacy and confidentiality. Homomorphic encryption allows third-party service providers or cloud platforms to process data without requiring access to the decrypted information. Here's how homomorphic encryption works: 1. Data encryption: The data is encrypted using a special encryption scheme that supports homomorphic operations. This encryption ensures that the data remains confidential and protected. -2. Computations on encrypted data: The encrypted data can undergo various computations and operations, such as addition, multiplication, or comparison, without decrypting it. The homomorphic encryption scheme allows these operations to be performed on the encrypted data directly. +2. Computations on encrypted data: The encrypted data can undergo various computations and operations, such as addition, multiplication, or comparison, without decrypting it. Data encrypted with homomorphic encryption can be directly manipulated. 3. Result decryption: Once the desired computations are completed, the final result is decrypted using a secret key known only to the data owner. The decrypted result reveals the outcome of the computations while keeping the original data secure. -Homomorphic encryption enables secure data processing in scenarios where privacy is of utmost importance. It has applications in various domains, including healthcare, finance, and data analysis. +Homomorphic encryption enables secure data processing in scenarios where privacy is of utmost importance. It finds applications in various domains, including healthcare, finance, and data analysis. Some advantages of homomorphic encryption include: -* Confidentiality: Homomorphic encryption ensures that sensitive data remains encrypted throughout the computation process, protecting it from unauthorized access. +* Confidentiality: Homomorphic encryption provides a robust safeguard by maintaining data encryption throughout computations, ensuring the protection of sensitive information from unauthorized access and preserving its confidentiality. * Outsourcing computations: Individuals or organizations can securely outsource computations to third-party providers without revealing the underlying data. -Homomorphic encryption is an exciting area of research with the potential to revolutionize data processing while maintaining privacy and security. It provides a practical solution for scenarios where secure computations on sensitive data are required. +Homomorphic encryption represents a captivating field of research with the potential to transform data processing by prioritizing privacy and security aspects. It provides a practical solution for scenarios where secure computations on sensitive data are required. To learn more about Homomorphic Encryption, [check here](/resources/homomorphic-encryption) **Conclusion**