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In this part, we will briefly explain the basic building blocks for the privacy-enhancing technologies that have been created over the past decades. The sort of security that PETs ensure relies upon the setting where they are utilized, for instance, privacy in smart mobility, or privacy of body & mind in smart health.
We start with the different privacy techniques that are used to develop privacy-friendly systems, and in most technologies, they are applicable.
How it works
Privacy by Design is a procedure to address the security issues in smart cities. Privacy by Design has seven principles that must be adhered to: proactive protection measures rather than remedial action after security breaches have occurred; security as the default setting; security incorporated into the design; full functionality with comprehensive security measures; security assurance throughout the entire lifecycle of the information; visibility and transparency; and respect for user privacy.
The most important part of any privacy-friendly system design is privacy testing and verification to ensure that the system’s design and implementation fulfill its privacy requirements. According to MITRE, security testing is fundamentally no different from other types of testing and should therefore be incorporated into existing testing processes. Testing approaches that are specific to security requirements aim to discover information leaks from applications, such as through black box differential fuzz testing or taint tracking, for instance, the analysis of data flow from sensitive program inputs to program outputs. The security properties of cryptographic protocols can also be formally verified using formal languages, e.g., applied pi calculus, using ontologies, or via model-based methods.
As we discussed above, the information minimization can be acquired from the privacy by design concepts. In brief, data minimization has been utilized to formally investigate structural decisions for electronic toll pricing and to determine privacy-preserving solutions for big data analysis.
Encryption maintains security by ensuring the privacy of messages or other information. Traditionally, symmetric encryption requires two parties to have a shared encryption/decryption key. On the other hand, public-key encryption allows encrypting the messages using a public key, and only the corresponding private key can decode the messages. Identity-based encryption is a type of public-key encryption where the public key can be an arbitrary string, such as a user’s name or email address. This allows messages to be encrypted for a receiver even if the receiver has not created a public/private key pair. Identity-based encryption can be used to enable private service discovery.
Secure multi-party calculation is a cryptographic technique that allows at least two parties to jointly compute the value of a public function without revealing their private data sources, and without relying on a trusted third party. Secure multi-party calculations provide privacy and unlinkability. They are computationally expensive, yet practical applications have been reported, for instance, to facilitate auctions where the final price can be computed without revealing individual bids. In smart cities, secure multi-party calculations can be used to devise healthcare solutions, for example, to compute the results of genomic tests where both the patient’s genome and the test sequence remain private.
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