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Definition of personal data and processing
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Personally Identifiable Information - Glossary Aggregated Information: Information elements collated on a number of individuals, typically used for the purposes of making comparisons or identifying patterns. Anonymized Information: Previously identifiable information that has been de-identified and for which a code or other association for re-identification no longer exists. Confidentiality: “Preserving authorized restrictions on information access and disclosure, including means for protecting personal privacy and proprietary information. Context of Use: The purpose for which PII is collected, stored, used, processed, disclosed, or disseminated. De-identified Information: Records that have had enough PII removed or obscured such that the remaining information does not identify an individual and there is no reasonable basis to believe that the information can be used to identify an individual. Distinguishable Information: Information that can be used to identify an individual. Harm: Any adverse effects that would be experienced by an individual (i.e., that may be socially, physically, or financially damaging) or an organization if the confidentiality of PII were breached. Linkable Information: Information about or related to an individual for which there is a possibility of logical association with other information about the individual. Linked Information: Information about or related to an individual that is logically associated with other information about the individual. Obscured Data: Data that has been distorted by cryptographic or other means to hide information. It is also referred to as being masked or obfuscated. Personally Identifiable Information (PII): ―Any information about an individual maintained by an agency, including (1) any information that can be used to distinguish or trace an individual‘s identity, such as name, social security number, date and place of birth, mother‘s maiden name, or biometric records; and (2) any other information that is linked or linkable to an individual, such as medical, educational, financial, and employment information. PII Confidentiality Impact Level: The PII confidentiality impact level—low, moderate, or high—indicates the potential harm that could result to the subject individuals and/or the organization if PII were inappropriately accessed, used, or disclosed. Privacy Impact Assessment (PIA): “An analysis of how information is handled that ensures handling conforms to applicable legal, regulatory, and policy requirements regarding privacy; determines the risks and effects of collecting, maintaining and disseminating information in identifiable form in an electronic information system; and examines and evaluates protections and alternative processes for handling information to mitigate potential privacy risks. System of Records: “A group of any records under the control of any agency from which information is retrieved by the name of the individual or by some ident.
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Personally Identifiable Information - Glossary Aggregated Information: Information elements collated on a number of individuals, typically used for the purposes of making comparisons or identifying patterns. Anonymized Information: Previously identifiable information that has been de-identified and for which a code or other association for re-identification no longer exists. Confidentiality: “Preserving authorized restrictions on information access and disclosure, including means for protecting personal privacy and proprietary information. Context of Use: The purpose for which PII is collected, stored, used, processed, disclosed, or disseminated. De-identified Information: Records that have had enough PII removed or obscured such that the remaining information does not identify an individual and there is no reasonable basis to believe that the information can be used to identify an individual. Distinguishable Information: Information that can be used to identify an individual. Harm: Any adverse effects that would be experienced by an individual (i.e., that may be socially, physically, or financially damaging) or an organization if the confidentiality of PII were breached. Linkable Information: Information about or related to an individual for which there is a possibility of logical association with other information about the individual. Linked Information: Information about or related to an individual that is logically associated with other information about the individual. Obscured Data: Data that has been distorted by cryptographic or other means to hide information. It is also referred to as being masked or obfuscated. Personally Identifiable Information (PII): ―Any information about an individual maintained by an agency, including (1) any information that can be used to distinguish or trace an individual‘s identity, such as name, social security number, date and place of birth, mother‘s maiden name, or biometric records; and (2) any other information that is linked or linkable to an individual, such as medical, educational, financial, and employment information. PII Confidentiality Impact Level: The PII confidentiality impact level—low, moderate, or high—indicates the potential harm that could result to the subject individuals and/or the organization if PII were inappropriately accessed, used, or disclosed. Privacy Impact Assessment (PIA): “An analysis of how information is handled that ensures handling conforms to applicable legal, regulatory, and policy requirements regarding privacy; determines the risks and effects of collecting, maintaining and disseminating information in identifiable form in an electronic information system; and examines and evaluates protections and alternative processes for handling information to mitigate potential privacy risks. System of Records: “A group of any records under the control of any agency from which information is retrieved by the name of the individual or by some ident.
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The uncontrollable flow of change in technology these days and use of data, information and knowledge is creating a huge challenges in the front of application User and developer both. Data breaches are happening in every sector and every level of all sectors. These challenges are countless starting from operational to strategic and becoming more challengeable day by day as the penetration of Information technology application among the common man is increasing. Therefore the threat is become real. Everybody customers or companies, retailer or stakeholders , distributor or dealer need assurance; from the provider. corporate face up reputational risks among the user at every step. So there is a need to understand the information technology, a frame work or body which can manage , risks and controls. A body or a system of Privacy management system is which can build a frame work for protection of the data and at the same time can maintain , privacy and agreement issues. This can be done by adoption of a scalable risk-based method which can determine what to be secured and how by performing the certain action.
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Personally Identifiable Information - Glossary Aggregated Information: Information elements collated on a number of individuals, typically used for the purposes of making comparisons or identifying patterns. Anonymized Information: Previously identifiable information that has been de-identified and for which a code or other association for re-identification no longer exists. Confidentiality: “Preserving authorized restrictions on information access and disclosure, including means for protecting personal privacy and proprietary information. Context of Use: The purpose for which PII is collected, stored, used, processed, disclosed, or disseminated. De-identified Information: Records that have had enough PII removed or obscured such that the remaining information does not identify an individual and there is no reasonable basis to believe that the information can be used to identify an individual. Distinguishable Information: Information that can be used to identify an individual. Harm: Any adverse effects that would be experienced by an individual (i.e., that may be socially, physically, or financially damaging) or an organization if the confidentiality of PII were breached. Linkable Information: Information about or related to an individual for which there is a possibility of logical association with other information about the individual. Linked Information: Information about or related to an individual that is logically associated with other information about the individual. Obscured Data: Data that has been distorted by cryptographic or other means to hide information. It is also referred to as being masked or obfuscated. Personally Identifiable Information (PII): ―Any information about an individual maintained by an agency, including (1) any information that can be used to distinguish or trace an individual‘s identity, such as name, social security number, date and place of birth, mother‘s maiden name, or biometric records; and (2) any other information that is linked or linkable to an individual, such as medical, educational, financial, and employment information. PII Confidentiality Impact Level: The PII confidentiality impact level—low, moderate, or high—indicates the potential harm that could result to the subject individuals and/or the organization if PII were inappropriately accessed, used, or disclosed. Privacy Impact Assessment (PIA): “An analysis of how information is handled that ensures handling conforms to applicable legal, regulatory, and policy requirements regarding privacy; determines the risks and effects of collecting, maintaining and disseminating information in identifiable form in an electronic information system; and examines and evaluates protections and alternative processes for handling information to mitigate potential privacy risks. System of Records: “A group of any records under the control of any agency from which information is retrieved by the name of the individual or by some ident.
Personally Identifiable Information - GlossaryAggregated Infor.docx
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ssuser562afc1
Personally Identifiable Information - Glossary Aggregated Information: Information elements collated on a number of individuals, typically used for the purposes of making comparisons or identifying patterns. Anonymized Information: Previously identifiable information that has been de-identified and for which a code or other association for re-identification no longer exists. Confidentiality: “Preserving authorized restrictions on information access and disclosure, including means for protecting personal privacy and proprietary information. Context of Use: The purpose for which PII is collected, stored, used, processed, disclosed, or disseminated. De-identified Information: Records that have had enough PII removed or obscured such that the remaining information does not identify an individual and there is no reasonable basis to believe that the information can be used to identify an individual. Distinguishable Information: Information that can be used to identify an individual. Harm: Any adverse effects that would be experienced by an individual (i.e., that may be socially, physically, or financially damaging) or an organization if the confidentiality of PII were breached. Linkable Information: Information about or related to an individual for which there is a possibility of logical association with other information about the individual. Linked Information: Information about or related to an individual that is logically associated with other information about the individual. Obscured Data: Data that has been distorted by cryptographic or other means to hide information. It is also referred to as being masked or obfuscated. Personally Identifiable Information (PII): ―Any information about an individual maintained by an agency, including (1) any information that can be used to distinguish or trace an individual‘s identity, such as name, social security number, date and place of birth, mother‘s maiden name, or biometric records; and (2) any other information that is linked or linkable to an individual, such as medical, educational, financial, and employment information. PII Confidentiality Impact Level: The PII confidentiality impact level—low, moderate, or high—indicates the potential harm that could result to the subject individuals and/or the organization if PII were inappropriately accessed, used, or disclosed. Privacy Impact Assessment (PIA): “An analysis of how information is handled that ensures handling conforms to applicable legal, regulatory, and policy requirements regarding privacy; determines the risks and effects of collecting, maintaining and disseminating information in identifiable form in an electronic information system; and examines and evaluates protections and alternative processes for handling information to mitigate potential privacy risks. System of Records: “A group of any records under the control of any agency from which information is retrieved by the name of the individual or by some ident.
Personally Identifiable Information - GlossaryAggregated Infor.docx
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JUST36
Christopher Millard Legally Compliant Use Of Personal Data In E Social Science
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This is an example training in the context of IS/DPP, information security, data protection and privacy. It is a training directed to procurement officers and outsourcing managers. The generic idea is that procurement officers and outsourcing managers support the inventory and overview of the company or group on third party relationships. By a well implemented governance through procurement officers and outsourcing managers it should be easier to upkeep the overview through the existing processes of managing (most) third party relationships, thus increasing ownership and awareness of information security and privacy.
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An example of how a template for a data protection impact assessment (art. 35 GDPR) could look like.
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Voorbeeld van een Nederlandstalige clausule die in een overheidsopdracht of Request for Proposal (RFP) kan worden ingesloten om alle verschillende mogelijke samenwerkingsvormen (joint controller, controller-to-controller of controller-to-processor) af te dekken of dat althans te pogen.
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Presentation given at the event organised by ACTE and BATM on 31 January 2019 addressing a few questions on the payments legislation that are relevant for travel and expense manager.
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Deze presentatie gaat in op de toepassing van de gegevensbeschermingswetgeving op verhuuractiviteiten.
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GDPR toegepast op huur-verhuur (Dutch)
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A presentation given at the legal hackers meetup of 19 June 2018 on common issues with controller-to-processor agreements aka "data processor agreement" (DPA). We revisit the distinction controller v processor. We then look at the directly applicable duties for processors, which do not need to be inserted in a contract. Finally we look at the different mandatory and "forgotten" components of the agreement.
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De slides van een presentatie voor makelaars in de verzekeringssector. Gepresenteerd op 12 juni 2018 voor de Kempische Verzekeringskring (https://www.kempischeverzekeringskring.be/activiteit/gdpr-wat-u-als-makelaar-nog-niet-wist/).
Gegevensbescherming makelaars
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Tommy Vandepitte
As the last speaker on the day after the Data Protection Day, I tried a different approach to the story of data protection and information security. I assembles a selection of movies, series, books (fiction and non-fiction) and games that any staff member should be able to go through themselves - as they please and at their own rhythm - and piece by piece learn about data protection and information security. In a way they can cultivate their own data protection awareness.
EEAS - Cultivate your data protection
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Presentation given on the experience of privacy design labs on the LSEC Belgium GDPR event of 30 November 2017. Event page: https://www.leadersinsecurity.org/events-old/icalrepeat.detail/2017/11/30/186/-/gdpr-plan-to-be-ready-prepare-to-set-change-to-go-session-3-privacy-impact-assessment-scenario-planning-data-loss-management.html?filter_reset=1 Privacy Design lab page: https://sites.google.com/site/pbd20171106 Example of a privacy design jam by Facebook (Berlin 2017) : https://www.facebook.com/facebookbrussels/videos/1419793831400471/
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Hoe breng je de nieuwigheden van de Algemene Gegevensbeschermingsverordening (AGV) of General Data Protection Regulation (GDPR) aan bij jouw stad of gemeente? Dit is een voorbeeld van slidedeck.
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This is an example of a deck for the decision makers (generally the board of directors) to first explain that data protection is a (reputational, legal, operational) risk that - like any other business risk needs to be managed. Then it allows for some explanation of the status of data protection (law) and the main novelties under the GDPR. It then highlights the main changes required in project mode and (later on, after the handover) in business-as-usual mode. Extra reference to the Vlerick reference (because published after the publication of this slide deck): http://www.vlerick.com/en/programmes/management-programmes/digital-transformation/digital-transformation-insights/insight-1)
GDPR project board deck (example)
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Tommy Vandepitte
An example of how the staff training on information security, data protection and privacy (IS/DPP) could look. This part is on an aspect that overarches all previous ones: monitoring. It touches on both perspectives of staff involvement: - staff works with the data, processes it, etc. and thus is the agent of the company - the company, to show accountability, should set up a balanced way of controlling the staff, which per se involves processing personal data of the staff members The slides come with notes that in short explain the visuals on the slides.
IS/DPP for staff #8 - Monitoring
IS/DPP for staff #8 - Monitoring
Tommy Vandepitte
An example of how the staff training on information security, data protection and privacy (IS/DPP) could look. This part is on incident management. How should staff react? How can an incident be effectively escalated? The slides come with notes that in short explain the visuals on the slides.
IS/DPP for staff #7 - Incidents
IS/DPP for staff #7 - Incidents
Tommy Vandepitte
An example of how the staff training on information security, data protection and privacy (IS/DPP) could look. This part is on the acceptable use of the companies (and sometimes also own) means. Each company should add what is appropriate for it. The slides come with notes that in short explain the visuals on the slides.
IS/DPP for staff #6 - Acceptable use
IS/DPP for staff #6 - Acceptable use
Tommy Vandepitte
An example of how the staff training on information security, data protection and privacy (IS/DPP) could look. The part focusses on authentication, and more particularly on passwords. The slides come with notes that in short explain the visuals on the slides.
IS/DPP for staff #5b - Passwords
IS/DPP for staff #5b - Passwords
Tommy Vandepitte
An example of how the staff training on information security, data protection and privacy (IS/DPP) could look. This part is on authorization and access rights, focussing on the staff's part in that. The slides come with notes that in short explain the visuals on the slides.
IS/DPP for staff #5a - Access
IS/DPP for staff #5a - Access
Tommy Vandepitte
An example of how the staff training on information security, data protection and privacy (IS/DPP) could look. This part is on data classification, drilling a bit deeper into confidentiality, integrity, availability (=CIA), privacy (=CAPI), traceability, and retention (=PATRIC), to be amended to meet the specific organisation's setup. The slides come with notes that in short explain the visuals on the slides.
IS/DPP for staff #3b - Data Classification
IS/DPP for staff #3b - Data Classification
Tommy Vandepitte
An example of how the staff training on information security, data protection and privacy (IS/DPP) could look. This part is on the concept of data, reasons for protecting data, personal data and data processing. The slides come with notes that in short explain the visuals on the slides.
IS/DPP for staff #3a - Data
IS/DPP for staff #3a - Data
Tommy Vandepitte
An example of how the staff training on information security, data protection and privacy (IS/DPP) could look. This part is on the reason why we should live up to the rules of IS/DPP, from a "negative" perspective (what do we want to avoid?) and from a "positive" perspective (what do we want to accomplish?). The slides come with notes that in short explain the visuals on the slides.
IS/DPP for staff #2 - Why?
IS/DPP for staff #2 - Why?
Tommy Vandepitte
An example of how the staff training on information security, data protection and privacy (IS/DPP) could look. This is an introduction explaining - the difference between information security, data protection and privacy, - the need and usefulness for staff engagement The slides come with notes that in short explain the visuals on the slides.
IS/DPP for staff #1 - intro
IS/DPP for staff #1 - intro
Tommy Vandepitte
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Presentation for the LSEC GDPR event - 20171130
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IS/DPP for staff #8 - Monitoring
IS/DPP for staff #8 - Monitoring
IS/DPP for staff #7 - Incidents
IS/DPP for staff #7 - Incidents
IS/DPP for staff #6 - Acceptable use
IS/DPP for staff #6 - Acceptable use
IS/DPP for staff #5b - Passwords
IS/DPP for staff #5b - Passwords
IS/DPP for staff #5a - Access
IS/DPP for staff #5a - Access
IS/DPP for staff #3b - Data Classification
IS/DPP for staff #3b - Data Classification
IS/DPP for staff #3a - Data
IS/DPP for staff #3a - Data
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IS/DPP for staff #2 - Why?
IS/DPP for staff #1 - intro
IS/DPP for staff #1 - intro
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Insurance IoT is a social good! Because: 🔵 Expected losses can be reduced structured programs with both real-time mitigation actions to solve a specific situation, and behavioral change mechanisms to promote safer behaviors (both in personal and commercial lines) 🔵 Rate can be matched to risks better: - Smaller and more accurate pricing clusters allow a large part of profiles to receive a lower rate - Even with the ability to price the individual probability of loss (cluster of one) the insurance foundation isn’t canceled (The risk of each policy will contribute to the expected losses of the portfolio; The premiums paid by many lucky policyholders will be used to pay the claims of the few unlucky policyholders who had an accident in a period of coverage) 🔵 IoT data can be used to increase the effectiveness and efficiency of the claim process, so improving the combined ratio ceteris paribus ⏩ Large part of the policyholders will pay less (and portfolios currently difficult to insure will become more insurable) Insurance IoT (and telematics) is not about providing a discount Insurance IoT (and telematics) is about creating economic value using the data and sharing part of this value with policyholders (and agents & brokers) An integrated holistic adoption of the IoT paradigm is a game changer for your insurance business and a social good: ◾ it allows to improve the availability and affordability of insurance coverages ◾while allowing the sector to write a profitable business Below you can enjoy a selection of best practices in both personal and commercial lines - written with some of the IoT Insurance Observatory members - that have already started to obtain results from their IoT business transformation journeys
Insurers' journeys to build a mastery in the IoT usage
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Dp2
1.
PERSONAL DATA PROTECTION
Scope Processing personal data Finality Legitimacy Transparency Organisation Proportional End-to-end
2.
3.
4.
Source: 32nd International
Conference of Data Protection and Privacy Commissioners (2010)
5.
6.
Editor's Notes
05/12/10