Privacy Models and Anonymisation Techniques Clause Samples

Privacy Models and Anonymisation Techniques. ‌ A number of technologies have been developed and advocated for data protection and privacy. These include privacy models and anonymisation methods, which aim at transforming data in such a way that they cannot be traced back to the individual data subjects to whom they refer, that is, such that subjects cannot be re-identified. Anonymised data should not be confused with de-identified data: de-identification merely refers to removing explicit identifiers from the data, but this may not be enough to prevent re- identification (e.g. a 17-year old widow is likely to be re-identifiable, even if her record has been de-identified by removing her name and passport number). Anonymisation goes beyond removing identifiers and perturbs or reduces the detail of quasi-identifiers (attributes that are not direct identifiers in isolation but that together may identify the subject, such as civil status, age and gender in the widow example). Current EU & US data protection laws do not apply to fully anonymised data. However, the forthcoming EU General Data Protection Regulation (GDPR) still applies to data protected in ways weaker than anonymisation, like pseudonymisation (replace identifiers by pseudonyms) or the above mentioned de- identification. Regarding protection of medical data, the U.S. regulations distinguish three categories: identified patient data sets, limited data sets, and anonymised data sets. Identified data sets (that is, fully original data sets containing patients’ identifiers) can only be released for research if broad informed consent from all patients has been obtained, which may be impractical. Limited data sets are those where 16 designated attributes have been removed; furthermore, users of limited data sets must sign a data use contract. Anonymised data sets improve data utility without decreasing protection with respect to limited data sets. They can be obtained in two accepted ways: either by applying the so-called safe harbour rules (which basically consist in removing or reducing the detail of 18 designated types of identifiers or quasi-identifiers) or by expert determination (by applying more sophisticated anonymisation methods). See an example application of safe harbour and expert anonymisation in ▇▇▇▇▇▇▇ D et al. (2016)16. Attributes can be classified in several categories depending on their privacy disclosure potential: identifiers and quasi-identifiers mentioned above, plus confidential (a.k.a. sensitive) attributes reporting sensiti...
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Related to Privacy Models and Anonymisation Techniques

  • Information Technology Accessibility Standards Any information technology related products or services purchased, used or maintained through this Grant must be compatible with the principles and goals contained in the Electronic and Information Technology Accessibility Standards adopted by the Architectural and Transportation Barriers Compliance Board under Section 508 of the federal Rehabilitation Act of 1973 (29 U.S.C. §794d), as amended. The federal Electronic and Information Technology Accessibility Standards can be found at: ▇▇▇▇://▇▇▇.▇▇▇▇▇▇-▇▇▇▇▇.▇▇▇/508.htm.

  • Information Technology Enterprise Architecture Requirements If this Contract involves information technology-related products or services, the Contractor agrees that all such products or services are compatible with any of the technology standards found at ▇▇▇▇▇://▇▇▇.▇▇.▇▇▇/iot/2394.htm that are applicable, including the assistive technology standard. The State may terminate this Contract for default if the terms of this paragraph are breached.

  • Information Technology The following applies to all contracts for information technology commodities and contractual services. “Information technology” is defined in section 287.012(15), F.S., to have the same meaning as provided in section 282.0041, F.S.

  • Accessibility of Information Technology Contractor represents and warrants that any software/ hardware/ communications system/ equipment (collectively “technology”), if any, provided under this Agreement adheres to the standards and/or specifications as may be set forth in the Section 508 of the Rehabilitation Act of 1973 standards guide and is fully compliant with WCAG 2.0 AA standards for accessibility and compliant with any applicable FCC regulations. Technology that will be used on a mobile device must also be navigable with Voiceover on iOS devices in addition to meeting WCAG 2.0 level AA. If portions of the technology or user experience are alleged to be non-compliant or non- accessible at any point, District will provide Contractor with notice of such allegation and Contractor shall use its best efforts to make the technology compliant and accessible. If a state or federal department, office or regulatory agency, or if any other third party administrative agency or organization (“Claimants”), make a claim, allegation, initiates legal or regulatory process, or if a court finds or otherwise determines that technology is non-compliant or non-accessible, Contractor shall indemnify, defend and hold harmless the District from and against any and all such claims, allegations, liabilities, damages, penalties, fees, costs (including but not limited to reasonable attorneys’ fees), arising out of or related to ▇▇▇▇▇▇▇▇▇’ claims. Contractor shall also fully indemnify District for the full cost of any user accommodation that is found to be necessary due to an identifiable lack of accessibility in the Contractor’s technology. If necessary, an independent 3rd party accessibility firm using POUR standards (Perceivable, Operable, Understandable and Robust) may be used to validate the accessibility of the technology.

  • Research Use Reporting To assure adherence to NIH GDS Policy, the PI agrees to provide annual Progress Updates as part of the annual Project Renewal or Project Close-out processes, prior to the expiration of the one (1) year data access period. The PI who is seeking Renewal or Close-out of a project agree to complete the appropriate online forms and provide specific information such as how the data have been used, including publications or presentations that resulted from the use of the requested dataset(s), a summary of any plans for future research use (if the PI is seeking renewal), any violations of the terms of access described within this Agreement and the implemented remediation, and information on any downstream intellectual property generated from the data. The PI also may include general comments regarding suggestions for improving the data access process in general. Information provided in the progress updates helps NIH evaluate program activities and may be considered by the NIH GDS governance committees as part of NIH’s effort to provide ongoing stewardship of data sharing activities subject to the NIH GDS Policy.