This document discusses privacy by design (PbD). PbD requires building privacy protections directly into systems and practices through principles like data protection by default and design. It involves implementing privacy-enhancing technologies and tools to empower users. One example is an identity protector that uses pseudonymization. PbD faces challenges in implementation due to lack of economic incentives and legacy systems. The document recommends a toolbox approach to PbD, with privacy impact assessments and patterns, as well as certification and standards to facilitate adoption.
Continuous PCI and GDPR Compliance With Data-Centric SecurityTokenEx
Continuous PCI and GDPR Compliance With Data-Centric Security describes how to develop a data security environment that is GDPR and/or PCI DSS compliant by utilizing tokenisation to pseudonymize sensitive data. Contact: Sales@tokenex.com
In this era, there are need to secure data in distributed database system. For collaborative data
publishing some anonymization techniques are available such as generalization and bucketization. We consider
the attack can call as “insider attack” by colluding data providers who may use their own records to infer
others records. To protect our database from these types of attacks we used slicing technique for anonymization,
as above techniques are not suitable for high dimensional data. It cause loss of data and also they need clear
separation of quasi identifier and sensitive database. We consider this threat and make several contributions.
First, we introduce a notion of data privacy and used slicing technique which shows that anonymized data
satisfies privacy and security of data which classifies data vertically and horizontally. Second, we present
verification algorithms which prove the security against number of providers of data and insure high utility and
data privacy of anonymized data with efficiency. For experimental result we use the hospital patient datasets
and suggest that our slicing approach achieves better or comparable utility and efficiency than baseline
algorithms while satisfying data security. Our experiment successfully demonstrates the difference between
computation time of encryption algorithm which is used to secure data and our system.
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.
Continuous PCI and GDPR Compliance With Data-Centric SecurityTokenEx
Continuous PCI and GDPR Compliance With Data-Centric Security describes how to develop a data security environment that is GDPR and/or PCI DSS compliant by utilizing tokenisation to pseudonymize sensitive data. Contact: Sales@tokenex.com
In this era, there are need to secure data in distributed database system. For collaborative data
publishing some anonymization techniques are available such as generalization and bucketization. We consider
the attack can call as “insider attack” by colluding data providers who may use their own records to infer
others records. To protect our database from these types of attacks we used slicing technique for anonymization,
as above techniques are not suitable for high dimensional data. It cause loss of data and also they need clear
separation of quasi identifier and sensitive database. We consider this threat and make several contributions.
First, we introduce a notion of data privacy and used slicing technique which shows that anonymized data
satisfies privacy and security of data which classifies data vertically and horizontally. Second, we present
verification algorithms which prove the security against number of providers of data and insure high utility and
data privacy of anonymized data with efficiency. For experimental result we use the hospital patient datasets
and suggest that our slicing approach achieves better or comparable utility and efficiency than baseline
algorithms while satisfying data security. Our experiment successfully demonstrates the difference between
computation time of encryption algorithm which is used to secure data and our system.
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.
Information security or Infosec worries with protecting information from unauthorized access. Its a part of information risk management and it therefore involves preventing or reducing the probability of unauthorized access, use, disclosure, disruption, deletion, corruption, modification, inspect or recording. In this article we will talk about the IT security, various threads to information security, different obstacles of information security and the various ways in which internet can be lucrative. Bhavya Verma | Purva Choudhary | Dr. Deepak Chahal "An Empirical Study on Information Security" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30888.pdf Paper Url :https://www.ijtsrd.com/computer-science/computer-security/30888/an-empirical-study-on-information-security/bhavya-verma
A survey on privacy preserving data publishingijcisjournal
Data mining is a computational process of analysing and extracting the data from large useful datasets. In
recent years, exchanging and publishing data has been common for their wealth of opportunities. Security,
Privacy and data integrity are considered as challenging problems in data
mining.Privacy is necessary to protect people’s interest in competitive situations. Privacy is an abilityto
create and maintain different sort of social relationships with people. Privacy Preservation is one of the
most important factor for an individual since he should not embarrassed by an adversary. The Privacy
Preservation is an important aspect of data mining to ensure the privacy by various methods. Privacy
Preservation is necessary to protect sensitive information associated with individual. This paper provides a
survey of key to success and an approach where individual’s privacy would to be non-distracted.
5 key steps for SMBs for reaching GDPR ComplianceGabor Farkas
In this GDPR Compliance presentation, you can learn more about the key steps to take for GDPR Compliance, including:
- What are data management processes and how to identify them at small and medium sized businesses
- What is personal data under the GDPR and how to establish a record of processing activities to map personal data
- How does encryption help with safeguarding personal data and ensuring GDPR compliance
- What your business should do to get ready for the new General Data Protection regulation on time
Data Anonymization Process Challenges and Context Missionsijdms
Data anonymization is one of the solutions allowing companies to comply with the GDPR directive in terms of data protection. In this context, developers must follow several steps in the process of data anonymization in development and testing environments. Indeed, real personal and sensitive data must not leave the production environment which is very secure. Often, anonymization experts are faced with difficulties including the lack of data flows and mapping between data sources, the non-cooperation of the database project teams (refusal to change) or even the lack of skills of these teams present due to the age of the systems developed by experienced teams who unfortunately left the project. Other problems are lack of data models. The aim of this paper is to discuss an anonymization process of databases of banking applications and present our context-based recommendations to overcome the different issues met and the solutions to improve methodologies of data anonymization process.
Increasing use of robots in domestic and care situations - eg in hospital, to help the aged at home, etc - combined with the likelihood that robots may surveille and record both the humans they aid and the general environment - leads to a need to think about the privacy implications of use of robots as carers or caring aids.
A Survey Paper on an Integrated Approach for Privacy Preserving In High Dimen...IJSRD
Data mining is a technique which is used for extraction of knowledge and information from large amount of data collected by hospitals, government and individuals. The term data mining is also referred as knowledge mining from databases. The major challenge in data mining is ensuring security and privacy of data in databases, because data sharing is common at organizational level. The data in databases comes from a number of sources like – medical, financial, library, marketing, shopping record etc so it is foremost task for anyone to keep secure that data. The objective is to achieve fully privacy preserved data without affecting the data utility in databases. i.e. how data is used or transferred between organizations so that data integrity remains in database but sensitive and confidential data is preserved. This paper presents a brief study about different PPDM techniques like- Randomization, perturbation, Slicing, summarization etc. by use of which the data privacy can be preserved. The technique for which the best computational and theoretical outcome is achieved is chosen for privacy preserving in high dimensional data.
ISSA Atlanta - Emerging application and data protection for multi cloudUlf Mattsson
Personal data privacy will be the most prominent issue affecting how businesses gather, store, process, and disclose data in public cloud. Businesses have been inundated with information on what recent privacy laws like GDPR and CCPA require, but many are still trying to figure out how to comply with them on a practical level. Many companies are focusing on data privacy from the legal and security side, which are foundational, but are missing the focus on data. The good news is that these data privacy regulations compel businesses to get a handle on personal data — how they get it, where they get it from, which systems process it, where it goes internally and externally, etc. In other words, the new norms of data privacy require proactive data management, which enables organizations to extract real business value from their data, improve the customer experience, streamline internal processes, and better understand their customers.
The new Verizon Data Breach Investigations Report (DBIR) provides perspectives on how Criminals simply shift their focus and adapt their tactics to locate and steal the data they find to be of most value.
This session will discuss Emerging Application and Data Protection for Multi-cloud and review Differential privacy, Tokenization, Homomorphic encryption, and Privacy-preserving computation.
• Learn New Application and Data Protection Strategies
• Learn Advancements in Machine Learning
• Learn how to develop a roadmap for EU GDPR compliance
• Learn Data-centric Security for Digital Business
• Learn Where Data Security and Value of Data Meet in the Cloud
• Learn Data Protection On-premises, and in Public and Private Clouds
• Learn about Emerging Application and Data Protection for Multi-cloud
• Learn about Emerging Data Privacy and Security for Cloud
• Learn about New Enterprise Application and Data Security Challenges
• Learn about Differential privacy, Tokenization, Homomorphic encryption, and Privacy-preserving computation
Digital Personal Data Protection (DPDP) Practical Approach For CISOsPriyanka Aash
Key Discussion Pointers:
1. Introduction to Data Privacy
- What is data privacy
- Privacy laws around the globe
- DPDPA Journey
2. Understanding the New Indian DPDPA 2023
- Objectives
- Principles of DPDPA
- Applicability
- Rights & Duties of Individuals
- Principals
- Legal implications/penalties
3. A practical approach to DPDPA compliance
- Personal data Inventory
- DPIA
- Risk treatment
Introduction of a New Non-Repudiation Service to Protect Sensitive Private DataIDES Editor
Current security systems dealing with sensitive
private data do not provide sufficient options to find data leaks.
An approach to find the last authorized receiver of a protected
copy is proposed in this paper. Existing security concepts are
extended by a new security service based on reliable tracking
data embedding. Additionally, a new mechanism to protect
the new tracking data is shown. Digital watermarking
techniques are used to provide tracking abilities for forwarded
copies of the protected data. This paper briefly describes
approaches to improve security for both the owner of protected
data and its recipients.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A de minimis rule for personal data breach notifications in the GDPRLiberty Global
This is the thesis that has been written by Machiel Bolhuis as part of the Executive Master of Science at the Cyber Security Academy (www.csacademy.nl) in The Hague, the Netherlands. The thesis focused on the development of a de minimis rule for the notification of personal data breaches that is included in the European General Data Protection Regulation (GDPR). Such a de minimis rule is part of a broader privacy risk classification framework, based on the risk-based approach that has been incorporated in the GDPR. Three categories of qualitative criteria can be identified to formulate a privacy risk classification framework for personal data breach notifications, i.e. nature of the affected data, nature and extent of the affected processing and adequate security measures to protect the personal data. A de minimis rule is formulated based on three criteria: (1) only public personal data is involved (2) it concerns one individual data source and (3) financial damage to the individual data subject as result of the breach is limited to € 500.
Design and Implementation of algorithm for detecting sensitive data leakage i...dbpublications
A dangerous threat the organization meet today is data loss. Data loss may happen intentionally or unintentionally. Intentional data loss happens when authorized person intentionally deletes the file or program. In unintentional data loss happens when the user unknowingly or accidently delete the file. Data loss causes financial damage to the organization. This may be direct loss or indirect loss. The cost of data loss relates to the value of data and also the amount of time it is unavailable. Unavailability of data poses serious problem to the organization. To avoid sensitive data loss a multilevel security system is to be developed to protect those valuable data. It is essential to discover the data leakage as soon as possible. The purpose of the research is to detect the leakage of sensitive data when it moves in a network. Here we proposed efficient novel algorithm is implemented using fingerprint method for detecting sensitive data leakage in a network.
Information security or Infosec worries with protecting information from unauthorized access. Its a part of information risk management and it therefore involves preventing or reducing the probability of unauthorized access, use, disclosure, disruption, deletion, corruption, modification, inspect or recording. In this article we will talk about the IT security, various threads to information security, different obstacles of information security and the various ways in which internet can be lucrative. Bhavya Verma | Purva Choudhary | Dr. Deepak Chahal "An Empirical Study on Information Security" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30888.pdf Paper Url :https://www.ijtsrd.com/computer-science/computer-security/30888/an-empirical-study-on-information-security/bhavya-verma
A survey on privacy preserving data publishingijcisjournal
Data mining is a computational process of analysing and extracting the data from large useful datasets. In
recent years, exchanging and publishing data has been common for their wealth of opportunities. Security,
Privacy and data integrity are considered as challenging problems in data
mining.Privacy is necessary to protect people’s interest in competitive situations. Privacy is an abilityto
create and maintain different sort of social relationships with people. Privacy Preservation is one of the
most important factor for an individual since he should not embarrassed by an adversary. The Privacy
Preservation is an important aspect of data mining to ensure the privacy by various methods. Privacy
Preservation is necessary to protect sensitive information associated with individual. This paper provides a
survey of key to success and an approach where individual’s privacy would to be non-distracted.
5 key steps for SMBs for reaching GDPR ComplianceGabor Farkas
In this GDPR Compliance presentation, you can learn more about the key steps to take for GDPR Compliance, including:
- What are data management processes and how to identify them at small and medium sized businesses
- What is personal data under the GDPR and how to establish a record of processing activities to map personal data
- How does encryption help with safeguarding personal data and ensuring GDPR compliance
- What your business should do to get ready for the new General Data Protection regulation on time
Data Anonymization Process Challenges and Context Missionsijdms
Data anonymization is one of the solutions allowing companies to comply with the GDPR directive in terms of data protection. In this context, developers must follow several steps in the process of data anonymization in development and testing environments. Indeed, real personal and sensitive data must not leave the production environment which is very secure. Often, anonymization experts are faced with difficulties including the lack of data flows and mapping between data sources, the non-cooperation of the database project teams (refusal to change) or even the lack of skills of these teams present due to the age of the systems developed by experienced teams who unfortunately left the project. Other problems are lack of data models. The aim of this paper is to discuss an anonymization process of databases of banking applications and present our context-based recommendations to overcome the different issues met and the solutions to improve methodologies of data anonymization process.
Increasing use of robots in domestic and care situations - eg in hospital, to help the aged at home, etc - combined with the likelihood that robots may surveille and record both the humans they aid and the general environment - leads to a need to think about the privacy implications of use of robots as carers or caring aids.
A Survey Paper on an Integrated Approach for Privacy Preserving In High Dimen...IJSRD
Data mining is a technique which is used for extraction of knowledge and information from large amount of data collected by hospitals, government and individuals. The term data mining is also referred as knowledge mining from databases. The major challenge in data mining is ensuring security and privacy of data in databases, because data sharing is common at organizational level. The data in databases comes from a number of sources like – medical, financial, library, marketing, shopping record etc so it is foremost task for anyone to keep secure that data. The objective is to achieve fully privacy preserved data without affecting the data utility in databases. i.e. how data is used or transferred between organizations so that data integrity remains in database but sensitive and confidential data is preserved. This paper presents a brief study about different PPDM techniques like- Randomization, perturbation, Slicing, summarization etc. by use of which the data privacy can be preserved. The technique for which the best computational and theoretical outcome is achieved is chosen for privacy preserving in high dimensional data.
ISSA Atlanta - Emerging application and data protection for multi cloudUlf Mattsson
Personal data privacy will be the most prominent issue affecting how businesses gather, store, process, and disclose data in public cloud. Businesses have been inundated with information on what recent privacy laws like GDPR and CCPA require, but many are still trying to figure out how to comply with them on a practical level. Many companies are focusing on data privacy from the legal and security side, which are foundational, but are missing the focus on data. The good news is that these data privacy regulations compel businesses to get a handle on personal data — how they get it, where they get it from, which systems process it, where it goes internally and externally, etc. In other words, the new norms of data privacy require proactive data management, which enables organizations to extract real business value from their data, improve the customer experience, streamline internal processes, and better understand their customers.
The new Verizon Data Breach Investigations Report (DBIR) provides perspectives on how Criminals simply shift their focus and adapt their tactics to locate and steal the data they find to be of most value.
This session will discuss Emerging Application and Data Protection for Multi-cloud and review Differential privacy, Tokenization, Homomorphic encryption, and Privacy-preserving computation.
• Learn New Application and Data Protection Strategies
• Learn Advancements in Machine Learning
• Learn how to develop a roadmap for EU GDPR compliance
• Learn Data-centric Security for Digital Business
• Learn Where Data Security and Value of Data Meet in the Cloud
• Learn Data Protection On-premises, and in Public and Private Clouds
• Learn about Emerging Application and Data Protection for Multi-cloud
• Learn about Emerging Data Privacy and Security for Cloud
• Learn about New Enterprise Application and Data Security Challenges
• Learn about Differential privacy, Tokenization, Homomorphic encryption, and Privacy-preserving computation
Digital Personal Data Protection (DPDP) Practical Approach For CISOsPriyanka Aash
Key Discussion Pointers:
1. Introduction to Data Privacy
- What is data privacy
- Privacy laws around the globe
- DPDPA Journey
2. Understanding the New Indian DPDPA 2023
- Objectives
- Principles of DPDPA
- Applicability
- Rights & Duties of Individuals
- Principals
- Legal implications/penalties
3. A practical approach to DPDPA compliance
- Personal data Inventory
- DPIA
- Risk treatment
Introduction of a New Non-Repudiation Service to Protect Sensitive Private DataIDES Editor
Current security systems dealing with sensitive
private data do not provide sufficient options to find data leaks.
An approach to find the last authorized receiver of a protected
copy is proposed in this paper. Existing security concepts are
extended by a new security service based on reliable tracking
data embedding. Additionally, a new mechanism to protect
the new tracking data is shown. Digital watermarking
techniques are used to provide tracking abilities for forwarded
copies of the protected data. This paper briefly describes
approaches to improve security for both the owner of protected
data and its recipients.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A de minimis rule for personal data breach notifications in the GDPRLiberty Global
This is the thesis that has been written by Machiel Bolhuis as part of the Executive Master of Science at the Cyber Security Academy (www.csacademy.nl) in The Hague, the Netherlands. The thesis focused on the development of a de minimis rule for the notification of personal data breaches that is included in the European General Data Protection Regulation (GDPR). Such a de minimis rule is part of a broader privacy risk classification framework, based on the risk-based approach that has been incorporated in the GDPR. Three categories of qualitative criteria can be identified to formulate a privacy risk classification framework for personal data breach notifications, i.e. nature of the affected data, nature and extent of the affected processing and adequate security measures to protect the personal data. A de minimis rule is formulated based on three criteria: (1) only public personal data is involved (2) it concerns one individual data source and (3) financial damage to the individual data subject as result of the breach is limited to € 500.
Design and Implementation of algorithm for detecting sensitive data leakage i...dbpublications
A dangerous threat the organization meet today is data loss. Data loss may happen intentionally or unintentionally. Intentional data loss happens when authorized person intentionally deletes the file or program. In unintentional data loss happens when the user unknowingly or accidently delete the file. Data loss causes financial damage to the organization. This may be direct loss or indirect loss. The cost of data loss relates to the value of data and also the amount of time it is unavailable. Unavailability of data poses serious problem to the organization. To avoid sensitive data loss a multilevel security system is to be developed to protect those valuable data. It is essential to discover the data leakage as soon as possible. The purpose of the research is to detect the leakage of sensitive data when it moves in a network. Here we proposed efficient novel algorithm is implemented using fingerprint method for detecting sensitive data leakage in a network.
1. PRIVACY-BY-DESIGN
Built-in Privacy Protection Inevitable
And Making Open Data Feasible
Dr. John Borking
Of counsel CMS Derks Star Busmann. & elaw Researcher University Leiden
25-2-2013 ePSI Warsaw 1
2. That’s me ≠ I am personal data
• Dr. J. J. Borking * 1945 - Director /Owner
Borking Consultancy Wassenaar Netherlands
• Of counsel Privacy-by-Design Law firm CMS
Derks Star Busmann in Utrecht
• EU/ CEN/ NR Researcher & Researcher e-Law
University of Leiden
• Arbitrator/ Mediator SGOA (ADR- ICT)
• Former Privacy Commissioner & Board Member
Dutch Data Protection Authority & Former
Board member Gaming & Lotteries Authority
• Senior Counsel Europe Xerox Corp
25-2-2013
ePSI Warsaw 2
3. WHAT IS PRIVACY BY DESIGN?
• Article 23 of the Draft Regulation requires “data
protection by design” and “data protection by default”.
(DPbD is applauded as a core innnovation of reform (Albrecht Report
2012/011 (COD))
• Privacy or Data protection or Compliance-by-Design?
• Having regard to the state of art and the cost of implementation, the controller shall, both at the time of the
determination of the means for processing and at the time of the processing itself, implement appropriate
technical and organizational measures and procedures in such a way that the processing will meet the
requirements of this Regulation and ensure the protection of the rights of the data subject.
• The controller shall implement mechanisms for ensuring that, by default, only those personal data are
processed which are necessary for each specific purpose of the processing and are especially not collected
or retained beyond the minimum necessary for those purposes, both in terms of the amount of the data and
the time of their storage. In particular, those mechanisms shall ensure that by default personal data are not
made accessible to an indefinite number of individuals.
25-2-2013 ePSI Warsaw 3
4. WHAT IS PRIVACY BY DESIGN?
The objective is:
“Privacy assurance must ideally become an
organization’s default mode of operation (…)
by deploying PETs (…) extending to a trilogy
of encompassing applications: 1. IT
systems; 2. Accountable business practices;
3.Physical design and networked
infrastructure.”
http://www.ipc.on.ca/images/Resources/7 foundational principles.pdf
25-2-2013 ePSI Warsaw 4
5. WHAT IS PRIVACY BY DESIGN?
• WP 168 The Future of Privacy p.13:
The application of such principle would
emphasize the need to implement privacy
enhancing technologies (PETs), privacy by
default settings and the necessary tools to
enable users to better protect their
personal data (e.g. access controls,
encryption).
• Achieving transparency and opacity
25-2-2013 ePSI Warsaw 5
6. PRIVACY BY DESIGN -WHAT IS IT?
INFORMATION TECHNOLOGY
PRIVACY SUPPORTING
ARCHITECTURE
PRIVACY ENHANCING
TECHNOLOGIES
DESIGN ORGANIZATION EXPERIENCE
GUIDED BY OF PRIVACY,
PRIVACY PRIVACY SUPPORTING BUSINESSES ESSENTIAL
PRINCIPLES & PROCESSES & PIAs FOR PRIVACY,
AND VALUES MANAGEMENT SUPPORT FOR TRUST AND
PRIVACY ADOPTION
M. Van Lieshout
PHYSICAL ENVIRONMENT Stimulerende en
PRIVACY SUPPORTING ORGANIZ- remmende factoren
van Privacy by Design
ATION OF PHYSICAL SPACES in Nederland (2012)
25-2-2013 ePSI Warsaw 6
7. ONE EXAMPLE OF PbD:
The Identity Protector as Design Pattern
USER
PID 1
KNOWN
PID 2
THE IDENTITY PROTECTOR (IDP)
PID n
IDENTITY DOMAIN PSEUDO IDENTITY DOMAIN
Borking J., Der Identity-Protector, in Datenschutz
ePSI Warsaw und Datensicherheit (DuD) 1996, 11 7
8. ONE PRACTICAL EXAMPLE OF PbD: Hospital Information System
Hospital Information System Basic tables with Pseudo Identities & ID Domains
patient caretaker
seq_patient Care relation seq_caretaker
patient_number seq_care relation crt_number
nac seq_patient crt_name
pid_caretaker
date_from
date_till Van Blarkum
No links 1997 &
between Borking, 2010
tables due
admission
to IDP
seq_admission
pid_carerelation
date_from
date_till
anamnesis medication etc notes
seq_anamnesis seq_medication seq_etc seq_notes
seq_admission seq_admission seq_admission seq_admission
pid_caretaker pid_caretaker pid_caretaker pid_caretaker
details details details text
25-2-2013ePSI Warsaw 8
9. HOSPITAL INFORMATION SYSTEM
ID Domain 3 till n for research and open data purposes
patient Care relation
seq_patient Care relation Care taker
patient_number seq_patient seq_care -taker
nac pid_care taker crt_number
date_from crt_name
date_till
ID domain 1 ID domain 2
Pseudo domain admission
3 till n seq_admission
pid_care taker
date_from
date_till
anamnesis medication etc notes
seq_anamnesis seq_medication seq_etc seq_notes
seq_admission seq_admission seq_admission seq_admission
pid_ care taker pid_ zcare taker pid_ care taker pid_ care taker
details details details text
25-2-2013 9
10. PROBLEMS FOR PRIVACY BY DESIGN
• PbD is done mostly without a proper privacy risk
analysis up front (PIA) ( J.J. Borking, Privacy Law is Code 2010)
• The translation of PbD (the legal specs +) into actual
designs of systems is done by example. Therefore,
everybody is free to postulate a particular design
(process) as “Privacy or Data Protection by Design”
(Van Rest Designing Privacy by Design 2012)
• On top of that, actual implementation is confronted
with difficulties such as lack of economic incentives,
transparency of systems, legacy systems, and lack of
adoption by organizations/end-users and consumers in
PbD. ( J.J. Borking, Privacy Law is Code 2010)
25-2-2013 ePSI Warsaw 10
11. HOW FURTHER?
• Do we let each designing party (industry and government) decide
per case or product line what PbD means (an evolutionary
approach? Each different party implements PbD in its own way),
(Van Rest Designing Privacy by Design 2012) or
• As we don’t know enough of and cannot leave it to (behavioral)
economics, that urgently justifies the need for EU Commission/
government/ data protection authority (DPA) involvement,
• “to adopt delegated acts (…) for the purpose of specifying any
further criteria and requirements for appropriate measures and
mechanisms referred to in paragraph 1 and 2 ( of article 23
GDPR), in particular for data protection by design requirements
applicable across sectors, products and services.
• The Commission may lay down technical standards for the
requirements laid down in paragraph 1 and 2)
25-2-2013 ePSI Warsaw 11
12. RECOMMENDATIONS
• PbD: No one-size-fits-all solution (Van Rest Designing Privacy by Design 2012)
• We need a toolbox with PIA, Privacy Design Patterns, PETs, PMS and design
processes (Waterfall etc.)
• Validate (use of) PbD-toolbox via design processes
• We do need the collection of and publication of concrete examples to learn
from and collect and create metrics for the consequences of PbD
• PbD: Should facilitate certification of [product, production
process, design], like certificates from EuroPrise &
Certification should proof the presence of PbD (a sine qua
non)
• Adoption of PbD should be promoted strongly by the DPA
& Behavioural Economics on Privacy should be researched
25-2-2013 ePSI Warsaw 12