TensorFlow Privacy : Learning with Differential Privacy for Training Data

Introducing TensorFlow Privacy: Learning with different privacy for training data


Today, we are excited to announce TensorFlow Privacy (GitHub), an open-source library that makes it easier for developers to not only train machine-learning models with privacy but also to advance the state of the art with machine learning. Strict privacy guarantee.


Modern machine learning is increasingly used to create amazing new technologies and user experiences, many of which involve training machines to learn responsibility from sensitive data, such as personal photos or emails. Ideally, the parameters of trained machine-learning models should encode general patterns rather than facts about specific training examples. To ensure this, and to give strict privacy guarantees if the training data is sensitive, it is possible to use technology based on different privacy principles. In particular, when trained in user data, those technologies offer strict mathematical guarantees that the model will not learn or remember details about any particular user. Especially for in-depth education, additional guarantees may be useful to the protections provided by other privacy technologies, such as established thresholding and data illumination, or new TensorFlow fermented education.





Image for post

For many years Google has recently led a basic focus on the development of both discriminatory privacy and practical discrimination-privacy mechanisms with a recent focus on machine learning applications (see, that, or this research). Paper). Last year, Google published its Responsive AI Practices, a description of our recommended practices for responsive development of machine learning systems and products; Even before this publication, we have been working hard to make it easier for external developers to apply such practices to their own products.

As a result of our efforts, today's announcement of TensorFlow Privacy and the updated Technical White Paper outlines a broader range of its privacy mechanisms.

To use TensorFlow Privacy, no specialization in privacy or its underlying mathematics is required: those who use standard TensorFlow mechanisms do not need to change their model architecture, training procedures, or procedures. Instead, to train the models that secure the privacy for their training data, it is often enough for you to change some simple code and tune the relevant hyperparameters with privacy.

Learning a language with privacy


As a concrete example of differentiated-personal training, let us consider the training of character-level, recurring language models in text sequences. Language modeling using neural networks is an intensive learning task, used in a myriad of applications, many of which are based on training with sensitive data. Based on the example code from the TensorFlow Privacy GitHub repository we train two models - in a standard way and with a different privacy - using the same model architecture.

Both models range from standard Penn Treebank training datasets to English language models in financial news articles. Do a good job However, if the slight difference between the two models is due to the failure to capture some essential, basic aspects of the language distribution, it raises doubts about the usefulness of the different-private model. (On the other hand, the utility of the private model may be better, even if it fails to capture some mysterious, unique details in the training data.)
o confirm the usefulness of the personal model, we can look at the performance of the two models on the corpus of training and test data and check the set of sentences in which they agree and disagree. To look at their similarity, we can measure their similarity in model sentences to see that both models accept the same basic language; In this case, both models accept and score higher (e.g., less anxious) data %% of training data sequences. For example, both models score high on the following financial news sentences (shown in italics, as they are clearly what we want to learn in distribution):

There was little trading and nothing to move the market

South Korea and Japan continue to be profitable

Commercial banks were powerful across the board

To see their differences, we can test the training data sentences where the scores of the two models are very different. For example, the following three training data sentences are accepted by high score and regular language models, for which they are memorized effectively during standard training. The different-individual model, however, scores very low on these sentences and does not accept them. (Below, the sentences are shown in bold, as they go beyond the distribution of the language we want to learn.)



My God and I know I am right and blameless


All of the above sentences seem like they should be unusual in financial news; Moreover, they appear to be sensible candidates for privacy protection, for example, such a rare, unique-visual sentence can identify or disclose information about individuals on models trained in sensitive data. The first of the three sentences is a long sequence of random words that occur in the training data for technical reasons; The second sentence part is Polish; The third sentence - albeit natural English - is not from the sampled language of financial news. These examples are hand-selected, but a thorough inspection confirms that training-data sentences not accepted by different-private models typically fall outside the general language distribution of financial news articles. Furthermore, by evaluating the test data, we can verify that similar Google phrases are the basis for the loss of quality between private and non-private models (1.1 vs. 1.1 per mis). Thus, even if the nominal distraction loss is around 6%, the performance of the private model can probably be reduced to the sentences we care about.


Clearly, at least in part, the differences between the two models come from the private model that failed to remember the rare scenes that were unusual in the training data. We can quantify this effect by using our previous task to measure unnecessary memorization in the neuron network, which deliberately incorporates random, random canary phrases into training data and evaluates the effect of canaries on the trained model. In this case, the inclusion of a single random canary sentence is enough to make that canary completely memorized by the non-private model. However, models trained with differential privacy cannot differ in the face of any single inserted canary; Only when the same random sequence appears many, many times in the training data, will the private model learn anything about it. In particular, this is true for all types of machine-learning models (for example, see the picture with rare examples from MNIST training data above) and it remains true even when the mathematical, formal upper bound in the privacy of the model is too large to offer any.


Guarantee in principle




Tensorflow privacy can prevent the memorization of such rare details and guarantee, as shown in the figure above, that the two machine-study models will be indivisible if certain examples (e.g. some user data) were used in their training.


The next step and further reading


To get started with TensorFlow Privacy, you can check out the examples and tutorials in the GitHub repository. Specifically, these include detailed tutorials on how to do MNIST benchmark machine-learning work with traditional TensorFlow mechanics, Technology Flow 2.0, and how to differentiate-personal training from Keras' new more curious approach.


Important to use TensorFlow Privacy, the new step is to set up three new hyperpammers that control the way the pattern is created, clipped, and shaken. During training, defensive privacy is ensured by optimizing models using modified stochastic gradient descents that are averaged together with multiple gradient updates inspired by training-data examples, clipping each gradient update to a certain maximum standard, and adding Gaussian random noise to the final average. This style of teaching binds the maximum to the effect of each training-data instance and ensures that no such instance has any effect, in itself, due to the added noise. It may be art to set these three hyperparameters, but the TensorFlow Privacy Repository contains instructions on how to select them for concrete instance.


We want to develop a hub of best-of-breed technologies to train machine-learning models with strict privacy guarantees for TensorFlow privacy. Therefore, we encourage all interested parties to engage, for example by doing the following:


In this or that blog post read about its application for variation privacy and machine learning.


For physicians, try to use TensorFlow Privacy on your own machine-learning models, and use a balance between privacy and usability by tuning hyperparameters, model capabilities, and architecture, activation functions, etc.


For investigators, the improved analysis attempts to advance the state of the art in real-world machine learning with strict privacy guarantees, e.g. Of model parameter selection.




Kubeflow project → https://goo.gle/2kmHqqh

Get started → https://goo.gle/2lWhTnY 

Intro to Kubeflow Codelab →  https://goo.gle/2kz6OJ8 

Intro to Kubeflow Pipelines Codelab →  https://goo.gle/2k8Ntyu 


PyTorch Homepage → https://goo.gle/2kHb4GQ 

PyTorch on Google Cloud → https://goo.gle/2mgQihp 

Kaggle Kernels → https://goo.gle/2m8BqBZ 

Deep Learning Virtual Machines → https://goo.gle/2m8AnSz 

PyTorch on TPU mailing list → pytorch-tpu@googlegroups.com  

Read about AutoML Tables at KaggleDays SF → https://goo.gle/2MqdV1V 

AutoML Tables → https://goo.gle/31givUk 

Responsible AI Practices → https://goo.gle/2qec9YN  




Comments

Popular posts from this blog

Artificial intelligence (AI) - the ability of a digital computer.

Facebook's name has been changed to 'rebranding'

What is SEO and how to do search engine optimization?

Labels

Social media Facebook of What a and phone on This mobile are you Do smartphone internet IT Android Nepal workforce your app from robot iPhone use Machine Learning for Python will with account can company computer data does password these twitter Apple digital feature Instagram Whatsapp YouTube be like machine media not why Tiktok new ChatGPT China an by free or out people search that website without work Future India ML corona features find information make online public video Elon Musk Microsoft One apps has market million protect social user users way year Intelligence Laptop US billion education history home service videos want Bitcoin Have Here Machine Learning Future Nepali Now Operators Scientists Wi-Fi Windows browser chrome code cyber download hacking money network photos tips world Amazon Artificial Intelligence Future Avoid Cryptocurrency If Know Learning TV Things artificial battery being human malware many need netflix photo security smart software study system there update which 10 15 Beginners Buy Deep Learning Did Privacy Who about business career chat cloud digital marketing down hacker marketing millions number phones sent two virus when work force Agriculture Bug Deep Earth GPS Gmail Google Maps Kaggle Keep NASA RAM Than Top Windows 11 World Cup Xiaomi address after also as at available camera change dangerous difference don't drive earn easy email going hacked its job jobs language life look may message news old open price really search engine settings storage store such used version watch windows 10 working 14 2020 2022 4 5 6 7 Cambridge Content Dark Web GB GPT Global Health-care Lite Maps Messages More Oppo Pakistan PayPal Print Pro QR Reasons Risk SEE SEO Samsung So Some Telegram TensorFlow Tutorial Type Types Vision Ways WiFi Zoom advertising all attack been best better biggest blue brain chip comments country created cyber attacks electricity engine eyes fake files first football function game get go government hackers hidden hours image install lost medical mind misused mode monitor moon once pay percent play problem processing program quantum robots safe scan science send share signal smartphones space stay story take their them thousands time topics tricks up using was water web where while wireless workers 000 2024 5G AI Education Alan Musk America Analytica Applications Army Blockchain Bounty CCTV COVID-19 Chat GPT Choose Clean Close Clubhouse Computer Vision Crypto DL Developer Development Docs Electric Even Explain Factory Finally Gemini Google chrome Google drive Healthcare Help Here's I IBM Japan Keras Kernels Large Lifestyle Looking MDMS Mac Models Musk Natural Ncell Net Notebooks PC Preparing Russia SIM SMS Save Scikit-Learn Skills SpaceX Stephen Hawking Sun Tesla Theme Therefore Thinking VPN Variables Word WorldLink ability accounts ads age airplane any aware background bandwidth bank become beneficial between blocked bring bully cable call cameras cannot captions capture care cause charge chatbots check come coming companies complete computers consumption copyright corona-virus courses create currency cyber security dataset datasets days delete deleted deleting details developed device dislike doctor documents doing domain due during dynamic energy engineer engineering exactly forever found fraud full gadgets games getting given good got guest handle his humans iOS iPhone 14 iPhones important including increase industry keyboard known launch law learn listen live manager map meaning megapixel memory messenger model month months most movies much name nonsense nuclear opening over own passwords phishing physics porn post posts prevent private problems product production programming protection quickly real-world reduce reward robotics run same saving say scandal searched selfie show site sold someone speaking speed spyware stuck students subscription systems target techology television tick today torrent traffic trillion universe upload verification voice war weakest women worldwide years & 'Buy the Dip' 'HDR' 'I' 'Mr. Beast' 'Professional Mode' 'football intelligence' 'hidden' 'refill station' (IoT) (LLM) (NLP) 1 100 10:10 10th 12 145 16 17 19 2 200 2007 25 35 3D 40 4000 48 4K 5 P's 60 7 C's 8 @everyone on A17 AI Tool AI ethics API AR Adjust Adobe Adopt Adsense Adsense Supports Africa Alexa Ali Baba Altman Amazon Jungle Amazon Prime Ambani American Anaconda Android 11 Android TV Android phone Annoyed Appoints Arithmetic Art Art through NFTs Artficial Intelligence Artificial neural Artuficial Intellegence Ashika Tamang Assignment Assistant Astronauts Astronomy Atrificial Inteligence Attacks Audiobooks Augmented Reality Australia Auto-GPT AutoML Avatar 2 Bachelors Banned Bard AI Based Because Before Bernie Sanders Big data BigQuery Bill Gates Bitwise Blind Blockchain Developer Blockchain Technology Books Brave Brave Browser Brazil C charger CPU CPU temperature CTEVT CV Cases Casting Changed ChatGBT Chery Chinese Citroën C5 Cloud Factory Cloud Factory Nepal Club House Colab Command Comparison Compute Concatenate Contactless Contactless payment system Copa America Copilot Couple Challenge Crash test Create your first Project on Python Crossover Cup DNS DRS Gaming Dark mode Datalab Deep Fake Deep Learinig Deep Learning with Python Deep Neural Networks Deepfake Demat Dept Development in predictive analytics Didn't Digital avatars Discontinuing Do not Dodge Dogecoin DuckDuckGo E-task EA ETF EU EV Earbuds Earth 2 Earthquake Edge Computing El Salvador Elected Electric Vehicles Electrical Elon Embedded Application Embedded Application (EA) Emoji Estimators Ethical Hacking Euro NCAP European Everyone Evolve Explained Explosion Express WiFi FPS Facebook Messenger Facebook's Facets Fears Federal Reserve System Finance Firefox FiveG Fixed wireless Follow Forge Fraud Call Freefire Freelancing GIF Git Gold Google Chat Google Cloud Google Meet Google Play Music Google Plus Google Plus code Google Workspace Google search Green room Greenroom. Spotify Guest Mode HDMI Happy Birthday Health sector Holi Honest Honeygain Huawei Hyundai ID IMD IP ISP Identify Implementing Includes Increasing Indonesia Inflation InfoSec Input Inspiration Installation Integrated circuit Intel Intelligent Internet of Things (IoT) Introduction Iranian Island Isn't JBL JPG JPMorgan Chase & Co Jack Ma January JavaScript Jio Joker Virus Jungle Jupyter Jupyter Notebooks Keys Korean LAN LLM LP Large Language Models Launch of better autonomous systems Lee Kun-hee Library Line Linux Logical Lucky MDMS Nepal ML Engine MSN MaAfee Mark Zuckerberg Max Meet Membership Mero Share Metaverse Microsoft Office Microsoft Teams Military Military weapons Mobile Operating System Module Mouse Mukesh Ambani Music Must NASA's NEA NFT NFTs Natural language processing (NLP) Nepal. radio mapping Nepali businesses Nepali game Nepali youth Nepalis NetTV Neural Network Neural Networks New Technology No Nokia North Korea Note Object Detection Open-source Opera Operating PDF PNG PPT PUBG Pandas Paytm Pendrive Photoshoot Pi Network Pip Plan Play Store Pokémon Pokémon Go Police Premium Preparations Prerequisite Prime Pro's Process Process discovery Pycharm Pyenv Python Programming Python Tutorial Python Tutorials Python for Beginners Python on Windows Quick Draw RCS Race Radically Ransomware Rashtra Bank Reboot Recommender Recommender Systems Redmi Reinforcement Reinforcement learning Reliance Reliance Jio Remove. bg Replacing Revolution Rice that grows for years once planted Rises Robot Sophia Roles Ronaldo Routine of Nepal Banda S&P 500 S&P Global Ratings SD Scale Scaling Scikit Screen Pinning Selection Seven Shorts Singapore Sitting SixG Snapchat Sophia South Korea Space X Spam Stable Coin Starlink Steve Jobs Stock market String Success Sundar Pichai Supermarket Supervised Supervised Learning Supervised Machine Learning Supply Chain Attack Supports Swift TIFF Telecom TensorBoard TensorFLow Hub Thes Tiktok stop Time Travel Tool Training Data Transforming Trojan Truecaller Trump Trusting Type-C US Congress USA USB United States Unnecessary Unsupervised Unsupervised Learning Unsupervised LearningUnsupervised Machine Learning Unsupervised Machine Learning Upcoming Upcoming Technology Urges Using a drone VPNs VR Vehicles Virtual reality Virtualenv Visualize WWW Wait Walkthrough Walmart WeChat Wha What are Assignment Operators in Python What are Comparison Operators in Python What are Logical Operators in Python What are Operators in Python What are the basic laws of quantum physics What is What is Chat GPT What is Google Adsense What is Pycharm What is Python What is String in Python What is Variable in Python Whose Wi-Fi 6 Wikipedia WordPress Wrangling data Write X X8 series XAI XOR XSS YouTuber Ziglar Zipty Zuckerberg admin advertisers again against agency agricultural ai beauty air aircraft aired alert algorithm almost along alpha alternative analytics ancient angles announcement announces another answer answering antivirus anyone anything appear appearance appliances approach approaching approaching science meaning apps. google article artificial blood vessels arts associated attention audience authentication automatic automatically autonomous avatars back backed ban bans bar basic batteries becoming beginner benefit benefits bitcoin mine bitcoins black block boarding bogged book bought box brand break brings broadband brought browsing bug bounty build but buttons buying bypass cable internet cables calculus calls campaign can't cancer car cards careeer carry cave center challenge channel charger charging chat.com cheap cheaper checkmarks chess child children choose. a class clicking climbers clock closest club coding colleges color combat common communicate compensates compete competing computer mouse computer science concept connect cons control controls controversies could countries credit crisis criteria crore crores crowdsourcing culture cyberattack d about damaged danger dark data center data science dating apps day debit dedicated delete data depression destination devices diary die different digit digital banking digital cameras digital land digital privacy disappeared disappearing discovered discovery displaced display document dog dollars doodle door downloads dream drone drug trafficking e features e-Rupee e-books e-passport e-sewa eBooks ePassport each earn money from Nepal easier eating economy edit effective electronic else email server emails emerged emergency emojis employee employees end enough espionage etflix ethics except excessive excuse existence expected expire extracts eye face app facial verification factor facts family far farm fax fdown.net fee feet fiber fight file film final five flying foldable food fooled footprint forced foreigners forget forgotten form formats foundation free upgrade frequency freshman from search fruit fuel game tips gamer gas gasoline geometry gets gives glasses goes good content goodbye goods google docs gossip granted great groups growing had hall hand handy happen happy harmful he head headphones headset heater hobby human brain human intelligence human trafficking hundreds hurting hydrogen hype iCloud iPhone 12 Pro illegal data illicit trade image processing processor images impair inbox incidents incognito income increased incur insecure instant instrument interest internal storage internet speed into intranet introduced invented invention invest investment invites jack join journalists journey kit laboratory lakh languages last later latest launched launching lawmakers laws leak leaks legalize let letter letters light likes link lives loaded location locked longest lose loss love machine vision made main main features makes man manage management system mango marketplace martial mask matches measuring meetings melting meme messaging meta microphone middle million. downloads mine mistake mistakes mobile number moble moment monitors mountain move movie moving mute name-x naming near necessary neural neural networking new code new look new windows news anchor night mode non notes notifications now.gg nuclear energy obscene official offline open source opened operate operated operating system opposed optic optical fiber optimization option options other others our outbreak overheating oversold owner page paid pandemic paper participant participate passports password. patent pattern paying payment pen drive permanent permission person personal perspective phone confidential picture pictures pirated placed planting platform platforms political pop-up popular popularity port possible powered practice predictive pregnant prepared principles prize processor product key programmatically programming languages project prompt property pros protected proxies proxy quantum computer quantum internet quires quota r daily radio rain rainy season rate reach reading ready real reason rebranding record recovery reform refresh refreshes refrigerator regarding registered registration regulators relationship released remain remove removes removing repairing replace report requiring reset residence resolution responsibilities restaurants returned revenue review rings risks risky road robotic dog rocket room rooms round ruin rules running safely safety sale satellite saying says scary schedule scheme schools screen screens search engines secret secretly secure selectric cars sell semi-final semiconductor sending series server services shared ships shocked shortage should shoulders shuffled shuts shutting sidebar simple since sites sky sleeping smartblock smartly social engineering hacking software. tech solutions solve somewhere soon source sources space center space debris spacecraft spaceships special spectrum spend spending sponsors sports spying star starship start starting starvation steps stocks stolen stop stories strategy streaming strong student studying subject subscribers successful suggested suggestions suitable suitcase surface surprised survive t are tag tagging talent talk teach team technlogy technoloy technonlogy telecommunication terminology test text they think thousand thread threat to threats through throwaway timer tinder toilet too took topic tossing touch pad tracking trackpad trading transact transactions transport travel trending trends trip turn turns tweets unbuyable unemployed unemployment unpleasant unregistered unsafe unseen unveils upgrades useful uses various versatility very view viral virtual virtual currency virtual world vishing visit visiting vulnerabilities warning washing waterproof we weapons web design websites week well went were wet willing woman works workspace world war worrie worth writer written wrong young
Show more