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Data oriented strategies Include which of the following?
Data-oriented strategies aim to protect data through various methods. The strategies listed under 'Minimize, Separate, Abstract, Hide' are focused on reducing the amount of data collected (Minimize), ensuring data is kept separate to avoid unintended access (Separate), abstracting data to limit exposure (Abstract), and hiding data to keep it concealed from unauthorized users (Hide). These strategies help in enhancing data privacy and security by applying principles of data minimization and access control. (Reference: IAPP CIPT Study Guide, Chapter on Data Protection Strategies and Techniques)
SCENARIO
Please use the following to answer the next questions:
Your company is launching a new track and trace health app during the outbreak of a virus pandemic in the US. The developers claim the app is based on privacy by design because personal data collected was considered to ensure only necessary data is captured, users are presented with a privacy notice, and they are asked to give consent before data is shared. Users can update their consent after logging into an account, through a dedicated privacy and consent hub. This is accessible through the 'Settings' icon from any app page, then clicking 'My Preferences', and selecting 'Information Sharing and Consent' where the following choices are displayed:
* "I consent to receive notifications and infection alerts";
* "I consent to receive information on additional features or services, and new products";
* "I consent to sharing only my risk result and location information, for exposure and contact tracing purposes";
* "I consent to share my data for medical research purposes"; and
* "I consent to share my data with healthcare providers affiliated to the company".
For each choice, an ON* or OFF tab is available The default setting is ON for all
Users purchase a virus screening service for USS29 99 for themselves or others using the app The virus screening
service works as follows:
* Step 1 A photo of the user's face is taken.
* Step 2 The user measures their temperature and adds the reading in the app
* Step 3 The user is asked to read sentences so that a voice analysis can detect symptoms
* Step 4 The user is asked to answer questions on known symptoms
* Step 5 The user can input information on family members (name date of birth, citizenship, home address, phone number, email and relationship).)
The results are displayed as one of the following risk status "Low. "Medium" or "High" if the user is deemed at "Medium " or "High" risk an alert may be sent to other users and the user is Invited to seek a medical consultation and diagnostic from a healthcare provider.
A user's risk status also feeds a world map for contact tracing purposes, where users are able to check if they have been or are in dose proximity of an infected person If a user has come in contact with another individual classified as "medium' or 'high' risk an instant notification also alerts the user of this. The app collects location trails of every user to monitor locations visited by an infected individual Location is collected using the phone's GPS functionary, whether the app is in use or not however, the exact location of the user is "blurred' for privacy reasons Users can only see on the map circles
What is likely to be the biggest privacy concern with the current 'Information Sharing and Consent' page?
The biggest privacy concern with the current 'Information Sharing and Consent' page is that all consent options are set to ON by default. According to privacy by design principles and data protection regulations, such as the General Data Protection Regulation (GDPR), consent should be freely given, specific, informed, and unambiguous. Pre-ticked boxes do not constitute valid consent because they do not provide a clear affirmative action from the user. The default ON setting could lead to unintentional data sharing and potential privacy breaches, making this a significant concern. (Reference: IAPP CIPT Study Guide, Chapter on Privacy by Design and Default)
In the realm of artificial intelligence, how has deep learning enabled greater implementation of machine learning?
Deep learning, a subset of machine learning, has enabled the greater implementation of machine learning by significantly enhancing the capabilities of neural networks. Here's how:
Neural Networks Expansion: Deep learning involves the use of large, complex neural networks that have many layers (hence the term 'deep'). These networks can model intricate patterns and representations in data.
Massive Data Processing: Deep learning algorithms require and utilize vast amounts of data to train these neural networks. The more data processed, the better the model can learn to generalize and perform accurately on new data.
Automatic Feature Extraction: Unlike traditional machine learning methods that often require manual feature extraction, deep learning algorithms can automatically learn and extract features from raw data. This eliminates the need for hand-coded classifiers and simplifies the process of implementing machine learning models.
Performance Improvements: The ability to process and learn from large datasets has led to breakthroughs in various fields such as image and speech recognition, natural language processing, and autonomous driving.
What privacy risk is NOT mitigated by the use of encrypted computation to target and serve online ads?
Option A: Encrypted computation focuses on protecting the privacy of data while allowing computations to be performed on it. It does not address the relevance of ads to users, which is a separate issue related to the effectiveness of the ad targeting algorithm.
Option B: Encrypted computation aims to protect the user's sensitive personal information by ensuring it remains encrypted during the computation process, thus mitigating this privacy risk.
Option C: Encrypted computation prevents the server from discerning personal information as the data remains encrypted throughout the process.
Option D: By maintaining encryption, encrypted computation also helps prevent information leaks due to weak de-identification techniques.
IAPP CIPT Study Guide
Research papers on encrypted computation and privacy-preserving ad targeting
These detailed explanations provide context and references to ensure the answers align with the IAPP Information Privacy Technologist documents and best practices.
What must be done to destroy data stored on "write once read many" (WORM) media?
To destroy data stored on 'write once read many' (WORM) media, the media must be physically destroyed. WORM media is designed to prevent data from being modified or erased once written. Therefore, the only effective method to ensure that the data is irretrievable is to physically destroy the media.
IAPP CIPT Study Guide: Data destruction methods for various storage media.
NIST SP 800-88: Guidelines for Media Sanitization, which recommends physical destruction for WORM media.
