Coordinated cookie practice review by German DPAs.

Observations on

• Wrong order – loading cookies/trackers prior to consent
• Wrong information – Insufficient or wrong information on user tracking on first level of the consent banner
• Wrong consent – Insufficient scope of consent. Many cookies/trackers remain active even if users deny consent on first level of banner to “All”
• No easy consent denial/revocation – Often no easy way to deny consent on first level of consent banner, or to close banner without a decision.
• Manipulation of users – dark design patterns, nudging..

Volltext (Online):
http://publica.fraunhofer.de/eprints/urn_nbn_de_0011-n-586394-15.pdf

hhttps://www.bwl.admin.ch/bwl/en/home/themen/ikt/ikt_minimalstandard.html

includes overview on regulatory requirements:

• MDR
• DVG
• DIGAV
• BSI 200-1 BSI-Standard 200-1, Managementsysteme für die Informationssicherheit
• BSI 200-2 BSI-Standard 200-2, IT-Grundschutz-Methodik
• BSI TR03161 Sicherheitsanforderungen an digitale Gesundheitsanwendungen
• ISO 27001:2017
• ISO/IEC 82304-1 Gesundheitssoftware – Teil 1: Allgemeine Anforderungen für die Produktsicherheit
• ISO/IEC 82304-2 Health Software – Part 2: Health and wellness apps – Quality and reliability [future – includes a “seal”]
• IEC 8001-5-1 Safety, security and effectiveness in the implementation and use of connected medical devices or connected health software – Part 5-1: Security – Activities in the product lifecycle

https://www.datenschutzkonferenz-online.de/media/dskb/TOP_30_Beschluss_Windows_10_mit_Anlagen.pdf

https://ukanon.net/framework/

English version: https://www.bsi.bund.de/EN/Publications/TechnicalGuidelines/TR03161/TechnicalGuidelines_03161_node.html
with direct link: https://www.bsi.bund.de/SharedDocs/Downloads/EN/BSI/Publications/TechGuidelines/TR03161/TR-03161.pdf?__blob=publicationFile&v=2

https://www.aepd.es/sites/default/files/2020-10/guia-proteccion-datos-por-defecto-en.pdf

• Nr. 1: Homeoffice (PDF)
  https://www.lda.bayern.de/media/checkliste/baylda_checkliste_homeoffice.pdf

• Nr. 2: Cybersicherheit für medizinische Einrichtungen (PDF)
  https://www.lda.bayern.de/media/checkliste/baylda_checkliste_medizin.pdf
  Interesting: “9 Externe Abrufmöglichkeit für Laborergebnisse” and “10 Fernwartung”

• Nr. 3: Patch Management (PDF)
  https://www.lda.bayern.de/media/checkliste/baylda_checkliste_patch_mgmt.pdf

• Nr. 4: Good Practice bei technischen und organisatorischen Maßnahmen nach Art. 32 DS-GVO (PDF)
  https://www.lda.bayern.de/media/checkliste/baylda_checkliste_tom.pdf

https://www.bitkom.org/sites/default/files/2020-10/201002_lf_anonymisierung-und-pseudonymisierung-von-daten.pdf

Examples given:

• Processing of geolocation profiles (movements)
• Google’s COVID-19 Community Mobility Reports
• De-coupled pseudonyms, e.g. for manufacurers remote monitoring machine performance at customers
• Speech recognition as example of federated learning
• Anonmyization and pseudonymization of medical text data using Natural Language Processing
• Use of sematic anonymization of sensitive data with inference-based AI and active ontolgies in the financial industry

Key words:

• Anonymization of structured data
  • Approaches
  • Aggregation approach
    • Generalization, Microaggregation
    • k-anonymity, l-diversity, t-closeness
    • Mondrian algorithm, MDAV method (Maximum Distance to Average Vector)
  • Randomization approach
    • Adding noise
  • Synthetic approach
    • (Creating a synthetic model based on original data to generate “matching” synthetic data)
  • Attacks
    • Was personal data of a known person used to genrate the anonymous data?
    • Which data in the anonymous data relates to personal data of a known person?
    • Predicting attributes of a known person
  • Static anonymization, Dynamic anonymization, Interactive anonymization
  • Pseudonymization
    • Format preserving encryption, Tokenization, Trusted third party, Pseudonymous Authentication (PAUTH), Oblivious transfer
  • Anonymization of texts
    • Ensure that free text inlcudes no identifying terms (e.g. via organizational measures)
    • Masking of identifying terms as part of post-processing
    • Structuring via Natural Language Processing
    • Caveat: Author might be identifiable based on writing style
  • Anonymization of multimedia data
  • Privacy via on-prem analysis and decentralization (see also: federated learning)
    • Homomorphic encryption: fully homomorphic, partially homomorphic, somewhat homomorphic
    • Secure multi-party computation
    • Garbled circuits
  • Privacy risks related to machine learning and controls
    • Identification of persons
    • Deanonmymization of data (e.g. of blurred images)
    • Memmbership inference
    • Model inversion
    • Defeating noise, others..
• Federated learning
  • (Moving algorithms to the local data – instead of moving data to central algorithm)
  • (Local data doesn’t leave device)
  • AI models as personal data
  • Legal advantages of federated learning
• Attacks and controls
  • Model inversion
    • Querying the trained AI model to reconstruct its training data
  • Membership inference
    • Was a given data point used to train the model?
  • Model extraction
    • “Stealing” the trained model – by cloning the behaviour and predictive capabilities of a given AI model
  • Adversial examples (creating inputs that trigger unintended responses)
  • Countermeasures
    • Restriction son outputs
    • Adversarial Regularization
    • Distillation
    • Differential Privacy
    • Cryptography
    • Secure multi-party computation (MPC)
    • Federated machine learning
    • Differential Private Data Synthesis (DIPS) (e.g. via Copula functions, Generative Adversarial Networks)