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Privacy by Design
- UNDER CONSTRUCTION*
Privacy by Design (PbD) is the practice of protecting privacy by means of processes, communication and technical measures as part of the software engineering design. This area is sitll young and there are several organisations and countried that have their own set of privacy principles. First, the most quotes set of principles:
7 Fundamental principles in Privacy by Design by professor Ann Cavoukian
- Proactive not reactive; Preventative not remedial
- Privacy as the default setting
- Privacy embedded into design
- Full functionality – positive-sum, not zero-sum
- End-to-end security – full lifecycle protection
- Visibility and transparency – keep it open
- Respect for user privacy – keep it user-centric
See also : [Principles of Privacy by Design] These are rather high level principles, academic and hard to interpret and apply. Here I have a list of concrete measures to match each of the 7 principles
- Anonymization of test data.
- As a counter example : Windows 10 has privacy settings that consumers have to enable, the settings violate privacy by default.
- (This seems a principle ad infinitum)
- If you turn on an ad-blocker, you should have the same privacy as without.
- Encrypted storage of consumer data
- Proper information on what personal data is used and for what purpoeses
- (another principle ad infinitum ?)
Organisation for Economic Co-operation and Development (OECD) principles for privacy are more practical ;
- Collection Limitation Principle There should be limits to the collection of personal data and any such data should be obtained by lawful and fair means and, where appropriate, with the knowledge or consent of the data subject.
- Data Quality Principle Personal data should be relevant to the purposes for which they are to be used, and, to the extent necessary for those purposes, should be accurate, complete and kept up-to-date.
- Purpose Specification Principle The purposes for which personal data are collected should be specified not later than at the time of data collection and the subsequent use limited to the fulfilment of those purposes or such others as are not incompatible with those purposes and as are specified on each occasion of change of purpose.
- Use Limitation Principle Personal data should not be disclosed, made available or otherwise used for purposes other than those specified.
- Security Safeguards Principle Personal data should be protected by reasonable security safeguards against such risks as loss or unauthorised access, destruction, use, modification or disclosure of data.
- Openness Principl There should be a general policy of openness about developments, practices and policies with respect to personal data. Means should be readily available of establishing the existence and nature of personal data, and the main purposes of their use, as well as the identity and usual residence of the data controller.
- Individual Participation Principle An individual should have the right: to obtain from a data controller, or otherwise, confirmation of whether or not the data controller has data relating to him; to have communicated to him, data relating to him
- Accountability Principle A data controller should be accountable for complying with measures which give effect to the principles stated above.
(text used from http://oecdprivacy.org/, with some omissions)
PETs or Privacy Enhancing Technologies
PII = Personal Identifiable Information
Fair Information Practice Principles (FIPPs)
As part of using digital personal data, good communication is important. The US Federal Trace Commission has some principles has the following rules :
- collection limitation
- data quality
- purpose specification
- use limitation
- security safeguards
- authentication uses at least a personal knowledge
- authentication uses at least a personal knowledge and something owned
- privacy sensitive data in-transit is sent encrypted, and stored encrypted on portable media
- services delivering privacy sensitive data is only accessible via authenticaiotn and authorization
- fysical access
- individal particpitation
Typical Privacy Anti-patterns
- Late aggregation : sub-optimal use of data by only using derived data
- Ask too much : using more data than is really used
- Keep too long : privacy sensitive data can only be held for the timespan the owner has given permission for.
- Scatter data : storing privacy sensitive data on several places makes it harder to keep data up to date, and clean when needed
- Trust all colleagues : inside a company, compartimentalization might also be needed to protect privacy.