• the quality or state of being apart from company or observation : seclusion
• freedom from unauthorized intrusion <one’s right to privacy>

⇒ right to be let alone

the claim of individuals … to determine for themselves when, how, and to what extent information about them is communicated to others. ~ Alan Westin (1967)

Modelling protection requirements (expectations) of classes of information as concentric circles of decreasing need for protection.

Cf. German constitutional court decisions about the absolute protection of the intimate sphere, that later got slightly diluted by the state trojan decision

• Assign data to corresponding spheres
• Assignment may depend on context and situation…

• Small snippets of information (probably) don’t expose a human
• Loss (and aggregation) of several snippets leads to a mosaic of the individual
• Increasing aggregation of puzzle pieces increases detail of knowledge on the individual
• Management of pieces that initially are not considered “intimate” possible
• Independence of the way data is lost (or: collected)
• Does not simplify determining criticality of pieces
• Considers data capture/collection, but also further data processing

• Humans act in roles depending on their situation 🎭
• Usually specific information required to achieve certain task ✅
• Group shared information according to context 🔂
• Personas, various levels of sensitivity 🗝️
• Individual images to be restrained to context ⭕
• Transfer through 3rd parties may cause unknown leaks ❗

• Linked to the privacy as roles model
• Idea that data is shared with a specific mindset in specific context

⇒ expected privacy

Nuremberg code → Ethics in Research → Privacy (in Research)

1. Required is the voluntary, well-informed, understanding consent of the human subject in a full legal capacity.
2. The experiment should aim at positive results for society that cannot be procured in some other way.
3. It should be based on previous knowledge (like, an expectation derived from animal experiments) that justifies the experiment.
4. The experiment should be set up in a way that avoids unnecessary physical and mental suffering and injuries.
5. It should not be conducted when there is any reason to believe that it implies a risk of death or disabling injury.
6. The risks of the experiment should be in proportion to (that is, not exceed) the expected humanitarian benefits.

“The claim of individuals, groups and institutions to
determine for themselves,
when,
how and
to what extent
information about them
is communicated to others”
(GDPR: is processed)

European law (GDPR) is based on that idea:

The sovereign (self-determined) citizen controls collection, use, and can effectively retract even previously openly published data, upon change of mind

Privacy wants to minimize trust assumptions

• Security Model
  • CIA
• Privacy Model
  • Sphere
  • Mosaic
  • Roles
  • Contextual Integrity
• Security Communication Model Attacker
  • Eve
  • Mallory
• Privacy Attacker
  • Passive, Active
  • Internal, External
  • Global, Local
  • …

are coherent measures that protect privacy by

• eliminating or reducing personal data or
• by preventing unnecessary/undesired processing of personal data
• without losing the functionality of the system

• collect and process personal data fairly and lawfully ⚖️
• purpose binding ➰
  • keep it only for one or more specified, explicit and lawful purposes
  • use and disclose it only in ways compatible with these purposes
• data minimization 🤏
  • adequate, relevant and not excessive wrt. the purpose
  • retained no longer than necessary
• transparency 🪟
  • inform who collects which data for which purposes
  • inform how the data is processed, stored, forwarded etc.
• user rights ✊
  • access to data
  • correction
  • deletion
• keep the data safe and secure 🔒

“personal data” shall mean any information relating to an identified or identifiable natural person (‘Data Subject’);

US: Name, address (Phone, Email), national identifiers (tax, passports), IP address, driving (vehicle registration, driver’s license), biometrics (face, fingerprints), credit card numbers, date/place of birth (age, login name(s), gender, “race”, grades, salary, criminal records)

EU: ‘personal data’ means any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person; [Art. 4, GDPR]

EU: ‘personal data’ means any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person; [Art. 4, GDPR]

Pseudonymous data can be linked back to an individual, and it hence is considered PII!

‘pseudonymisation’ means the processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately and is subject to technical and organisational measures to ensure that the personal data are not attributed to an identified or identifiable natural person; [Art. 4(5), GDPR]

• Data without any relation to individuals
  • Simulation data
  • Measurements from experiments
• Data with relation to individuals
  • Types
    • Content
    • Metadata
  • Revelation
    • Consciously
    • Unconsciously

• Explicit
  • Created content
  • Comments
  • Structural interaction (contacts, likes)
• Metadata
  • Session artifacts (time of actions)
  • interest (retrieved profiles; membership in groups/participation in discussions)
  • influence
  • Clickstreams, ad preferences
  • communication (end points, type, intensity, frequency, extent)
  • location (IP; shared; GPS coordinates)
• Inferred
  • Preference
  • Image recognition models
  • Personal details
• Externally correlated
  • Observation in ad networks

• Disclosure of identity
  • Identify an individual (in a dataset)
  • Link identity to an observation
• Disclosure of attributes
  • Infer a (hidden) attribute of an individual
  • Link additional information to identity

• Soft: Fully trust another party to keep data private in the user’s interest
  • TLS
  • Privacy settings on a web platform
• Hard: Semi-trusted participants, the goal is to reduce trust to a minimum and have mathematical privacy guarantees

Anonymity: “Anonymity of a subject means that the subject is not identifiable within a set of subjects, the anonymity set.”

we do not learn who sends something

we do not learn who sends which message (same receiver)

we do not learn who sends to whom (same message)

we do not learn who sends which message or to whom

• Sender Unobservability
• Passive external receiver
• Higher latency, management overhead
• Availability risk (blenders)

• Availability drawback: Cascades = single point of failure
• Improve Availability: Free-route mix networks
  • route is not fixed, any sequence of nodes from the network can be used for relaying messages

• Sender Unobservability
• Global passive adversary and up to n-2 corrupt participants
• High bandwidth overhead, collisions and DoS, scalability issues

• Data sources
  • Published data
  • Observable data
  • Repurposed data
  • All other data
• Input of metric
  • Prior knowledge of the adversary
  • Adversary’s resources
  • Adversary’s estimate
  • Ground truth/true outcome
  • Parameters

• We measure the privacy of the data release mechanism
• We cannot protect against adaptation of the prior (and corresponding inference)
• General: If statistics are revealed, they are useless or help improve the prior

A privacy model is composable if the privacy guarantees of the model are preserved (possibly to a limited extent) after repeated independent application of the privacy model. From the opposite perspective, a privacy model is not composable if multiple independent data releases, each of them satisfying the requirements of the privacy model, may result in a privacy breach

Sparse data, where only a few data points are sufficient to identify someone

• Drastic reduction of database
• Dangerous when used by itself

• Not generalizing all dimensions
• Inappropriate regions definition
• Background knowledge attacks
• Drastic reduction of precision
• Dangerous when used by itself

• Unpredictable biases
• Impossible trajectories

Not allowed to change entries that are far from each other, as in event-neighborhood

Statistical disclosure control (SDC) is the field that protects statistical databases so that they can be released without revealing confidential information that can be linked to specific individuals among those to which the data correspond

• SDC aims to provide respondent privacy
• Privacy-preserving data mining (PPDM) seeks database owner privacy
• Private information retrieval (PIR) aims for user/analyst privacy

Having two respondents is not enough.

Other respondents can collude.

When one person is known to be an outlier, and the average without them is known, then we can identify the max.

• Masking methods: generate a modified version of the original data
  • Perturbative: modify data
    • Noise addition, microaggregation, rank swapping, microdata rounding, and resampling
  • Non-perturbative: do not modify the data but rather produce partial suppressions or reductions of detail in the original dataset
    • Sampling, global recoding, top and bottom coding, and local suppression
• Synthetic methods: generate synthetic or artificial data with similar statistical properties

The Laplace mechanism is an output perturbation mechanism, as opposed to input perturbation mechanisms such as randomized response (RR)