Protecting private sector good public model-Protecting Online Privacy in the Private Sector: Is there a ‘Better’ Model? - Persée

Dubrovsky David. The theme of this essay competition is the relation between law and cyberspace. In addressing the struggle faced by governments and industry experts to identify effective approaches for regulating emerging technologies, the author compares the effectiveness of legislation and industry self regulation aimed at protecting online privacy. Three central issues are considered : consent, burden of protection and enforcement. The analysis suggests that neither course is mutually exclusive and that a consolidated approach provides a more effective level of protection and a more malleable framework to meet future needs.

Protecting private sector good public model

Data protection around the world. Efficiency of a policy is strongly dependent on Protecting private sector good public model between Dripping springs texas alamo involved in its implementation and these benefitting of it. Companies support the idea, because they claim to operate on a market where given prices are lower than costs of production what effectively blocks future investments [Paczewska, ]. Authors pointed out series of actions that enables public entities to face a crisis situation. This brief aims to provide answers to these questions and an overview of the approaches available to authorities seeking to maximize public access to their data. It was a barrier of entry and favored companies existing on the market over new ones. Contact Us Disclaimer Acknowledgements.

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Uses and Disclosures: Organizational Requirements. New Hampshire Business Review. But what if, despite vaccines, you get the flu? It is generally mosel that doctors own the medical Goid they keep about patients Protecting private sector good public model vs. Yet while disgruntled employees are a serious threat to government, so too are those who breach security through ignorance or complacency. Regensdorfer vs. Grandad fucking grandaughter have national probabilistic surveys e. Subscribe to receive more business insights, analysis, and perspectives from Deloitte Insights. From a private-sector perspective, Crown outlines some of the potential standardization, privacy, and statistical challenges associated with data aggregation and provides insight into the variety morel sources of clinical data. Most states apply a similar model to applications for health insurance NCSL, aand some states have extended that to disability and life insurance NCSL, b. Music and the Spoken Word. Email a customized link that shows your highlighted text. It would be possible to conduct research within the databases represented by each of the nodes through a standard research protocol, and then to pool the results.

How to effectively manage risk for the protection of facilities, technology assets and business operations.

  • It's been predicted that up to , public sector jobs could be cut by the end of
  • Is government up to the task?
  • Gone are the days, when only the Public Sector was prevalent in the economy.
  • These enormously complex contracts between government and businesses can accomplish what neither side can do alone—expanding infrastructure when funds are limited, for example.
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Sharing information is the foundation of all communication between people, from individual interactions to the relationships between whole societies, countries, and cultures. Whether through language, mathematics, music, visual arts, or—more recently—code, the effect is the same: the shaping of our individual and collective understanding of the world through the exchange of knowledge and experience.

The sharing of information and knowledge is becoming ever more critical to the shaping of human experience. The globalization of communications driven by advances in computer processing power, improved internet connectivity and speed, and the almost ubiquitous presence of mobile communications devices are largely responsible for this step change.

Huge quantities of diverse forms of information can now be processed and shared at previously unimaginable speeds. Under the hood of this unfolding digital revolution in the production, sharing, and use of information and knowledge lies an equally important revolution—the data revolution.

In , 2. The sharp scale of the change in the production of data globally is captured in the statistic that 90 percent of data in the world today was produced in the last two years ITU. The prospective benefits of these trends are recognized in the international development sector. The Data Revolution for Sustainable Development is now well established.

UN Data Revolution Group New partnerships have emerged seeking to harness the potential of this revolution and a dedicated community of practitioners [ii] operates worldwide exploring how to harness it to achieve development outcomes, such as the Sustainable Development Goals SDGs.

Numerous pieces of research and programs of work now center on the use of data to improve development processes and achieve and monitor the SDGs [iii] and feed into efforts to promote evidence-informed decision-making Jones In turn, the need for more data sharing to contribute to evidence-informed decision-making and the achievement of development outcomes has resulted in experimentation with numerous new models and innovations.

As quantities of data have increased around the world, calls for publicly-produced data to be made freely available have also increased. This right is further supported by the United Nations Fundamental Principles of Official Statistics, a set of ten principles that lay out the professional and scientific standards for national statistical offices NSOs. The first principle, which arguably incorporates the remaining nine and embraces the core principle of open data, states:.

There are also economic reasons for the growth of the open data movement. Government-produced data are public goods and though they may be expensive to produce, they create economic benefits when they are open.

Public goods are non-excludable, meaning that their use by one person does not reduce their availability for use by another. As a result, they can be used and reused many times, each time increasing their social and economic benefit from new products and services created or, more indirectly, from efficiency gains and the reduction of transaction costs Pollock In one of the earliest studies of the benefits of open data, Rufus Pollock estimated welfare gains to opening data that were previously sold by the British government to range between 1.

Despite there being a strong case for sharing, too often the data that governments produce is not shared with the public. The Open Data Inventory ODIN , an analysis of the availability of indicators that comprise the basic framework of statistical systems, showed that in only 29 out of the countries published data in all 21 data categories assessed such as health outcomes, international trade, national accounts, and pollution Open Data Watch Furthermore, 55 countries did not publish data in at least five categories, indicating substantial gaps in data sharing.

There are many reasons for failures to share public data. These include: the sometimes perceived complexity and effort needed to do so; a lack of political will or incentives to share data; shortfalls in the skills, human and institutional capacity; or a lack of financial resources and investment in public digital and data infrastructures, to name a few.

Pervasive and sometimes widening digital divides around the world further stifle efforts to open up and share publicly produced data—or at the very least disincentivize digital data use, and by implication public demand for disclosure—in places where internet access is still prohibitively expensive or digital literacy rates low World Wide Web Foundation This brief adopts a framework Figure 1 premised on the notion that the value of data can be significantly enhanced through responsible sharing: underpinned by effective and enforceable laws and policies, and cognizant of the need for both sustainable financing for data and investment in institutional capacity and skills.

Sets of principles and norms are emerging to help guide practitioners through this complex and ever-evolving space. This concept is the cornerstone that sets the stage for how countries and administrative authorities should approach data disclosure and sharing. However, this simple principle hides a far more complex reality. What are legitimate reasons for nondisclosure of public information? What are the alternative approaches to data sharing that can maximize public access to data that cannot otherwise be made open?

These are but some of the questions that practitioners working with the data revolution—whether statisticians in NSOs or intergovernmental agencies, development professionals responsible for knowledge management or open government advocates—are constantly juggling. This brief aims to provide answers to these questions and an overview of the approaches available to authorities seeking to maximize public access to their data. It then considers tools available to maximize data sharing when they cannot be made open.

It also explores administrative public-to-public data sharing and touches on public-to-private sharing, only insofar as the sharing of data with a private entity relates to the exercise of a public function in situations where public money is being spent. While the converse scenario, private-to-public data sharing, is hugely important to the achievement of development outcomes, it falls outside the scope of this brief but is suggested as an area for further research at the end of the paper, along with other opportunities for future research.

To understand how an open by default approach to data disclosure can be applied, it is necessary to understand how it is underpinned by access to information ATI law, which in some jurisdictions may be referred to as Freedom of Information law, and the legal links between ATI and the concepts of open data and data sharing.

By , the number stood at Loesche In most countries with ATI laws, they are the legislative foundation that authorize government bodies to disclose and share information and data with the public, and grant individuals the reciprocal right to access it.

In countries with robust ATI laws, the concept of making data open by default then emerges as a preferred policy approach to implementing and operationalizing the legal duty to proactively disclose information and data by creating a presumption in favor of openness—i.

There are a number of interrelated technical, legal, policy, and user considerations that contribute to the above definition and facilitate the ability to disclose and share data openly. These considerations are spread out between numerous tools and resources some of which are listed below and can be summarized thus:.

Data portals are one way providing public access to open data, although increasingly the use of linked-data approaches is recommended where the resources and capacity exist.

This requires active engagement with those to whom data is being disclosed. This can be achieved through having active feedback mechanisms that enable users to comment on datasets that are released Orrell As the prevalence of ATI laws has expanded, so too have debates about the extent and scope of public duties to disclose information and data. With the disclosure of public information—typically things such as government budgets, aggregated official statistics, organizational policies, etc.

The sharing of raw data via proactive disclosure and through an open by default approach however is another matter. Under an open by default approach, does an NSO have a duty to share the microdata that are used to compile official statistics? If so, in what form? It would enable analysts to combine multiple datasets using disaggregated characteristics or to look at the distribution of characteristics across a large population in a more precise manner, rather than relying on means and medians.

From a policy perspective, however, it is when the questions above are asked that the limitations of the open by default approach to data sharing becomes more apparent. While some statistical laws and NSO website terms of use will clarify exactly what can and cannot be shared, [xxix] and the Fundamental Principles of Official Statistics make clear that confidential information should never be shared, [xxx] there is still a significant grey area of uncertainty where clearer guidance is needed.

To understand whether it is possible to share data that fall into this grey area, first it is necessary to understand what the legitimate exemptions to the disclosure and sharing of public data are.

It is important to recognize that there are legitimate exemptions to the open by default approach to data sharing. Public bodies and authorities collect and compile information and data about almost all conceivable dimensions of society, from highly sensitive personal data collected by health authorities to critical intelligence information and strategic data that inform defense policy.

While it is perfectly reasonable for states to keep this confidential information hidden from the public, in order for the public to trust that any state-sanctioned secrecy or duty to protect data is conducted in the public interest, these processes must operate as transparently as possible with clear checks and balances in place to prevent abuse.

The first step to being transparent and accountable is clearly demarking what classes of information and data are not accessible to the public, explaining why, and ensuring that legally enforceable checks and balances are in place to prevent abuse of the system. Although classes of information that are withheld from the public differ from country to country, internationally recognized standards do exist. While there are a number of legitimate exemptions including information and data on national security matters, defense, and international relations, among others, two areas are of particular importance to NSOs, knowledge managers, and other practitioners within the development sector: personal information and confidential commercial information.

SPI or sensitive personal data include classes of information and data such as medical records, biometric data, and private financial information. Often, independent regulators are appointed to oversee the application of data protection laws to ensure that they enforced appropriately, such as the Information Regulator in South Africa.

This is problematic not only from a rights perspective, but also because it can have a chilling effect on the willingness of foreign entities to engage in data sharing activities in these countries in the absence of a robust regulatory framework.

These uncertainties have the potential to stifle and slow cross-border innovation and the application of data-driven technologies to achieve the SDGs where data protection safeguards are lax or nonexistent. In the UK, the Statistics and Registration Service Act the Act grants the Office for National Statistics ONS the authority to take a range of decisions around what types of entities it can partner with, how it can source data to compile statistics, and how to release them.

Notwithstanding this broad power, the Act contains numerous checks and balances throughout, ensuring that the ONS has the authority to take decisions that relate to the exercise of its legal functions, but that it is also accountable to the legislature and overseen by the executive. In this way, it is an example of a statistical law that balances NSO independence and openness with accountability.

The Act contains numerous provisions that relate specifically to data sharing, including intra-governmental data sharing. Very clear rules about how and when data can be shared between the ONS and the office responsible for civil registration, national health authority, and tax authority are set out.

In relation to health data in particular, very strict rules are set out about the classes of data that can be shared by the health authorities with the ONS. Section 43 of the Act sets out these rules in detail, granting permission to the Health Minister to share limited patient registration information with the ONS. One of the benefits of having very precise and clear rules around intra-governmental data sharing is that they provide the judiciary with a lot of legal certainty when disputes arise.

As a result, the government abandoned the policy. The balance of these attributes is ultimately what creates an open, transparent, and accountable environment in which data can be responsibly and safely shared across government. In many countries, certain public functions are routinely undertaken by private companies that are subcontracted by administrative authorities.

Subcontracted functions can range from infrastructure maintenance roads, the electricity grid, broadband Internet services, etc. Similarly, ATI laws should ordinarily be aligned with intellectual property legislation and protect copyright belonging to third parties where necessary. The legal interoperability of licensing structures [xii] both within countries and between jurisdictions is therefore of special importance here see Box 1: What is Open Data?

Now that the application and limitations of an open by default approach to data sharing have been outlined, it is time to return to the question of what options for data sharing exist where there is a grey area between data being open or not shared at all.

The remainder of this section covers interrelated practical approaches and tools to data sharing that can be used by NSOs and other entities engaged in development activities to share as much data as possible while respecting the need to protect personal and sensitive personal data as well as commercial confidentiality. Suppression: achieved by removing a personal or sensitive data field in essence the same process as redaction but one that can be programmed to be automated for specified fields ;.

Aggregation: achieved through the clustering of data together into larger units—for instance, representing salaries across a range of individuals as an average rather than a series of distinct data points;. Pseudonymization and unique identifiers: a variant of anonymization in which data that can be used to identify an individual name, age, etc. Perturbance: a method of protecting privacy through the changing of certain values while keeping key aggregates constant.

There are a number of techniques and tools available to practitioners seeking to make datasets containing personal or sensitive data as open as possible. They range from the fairly crude—severing tables and redacting documents—to the more complex use, notably use of de-identification techniques that can be automated.

Microdata—sets of records containing information on individual persons, households or business entities—have potential on their own to fill current data gaps, enable additional disaggregation of populations and localities, establish baselines, or provide ongoing monitoring for sustainable development. Since microdata sets can contain PII, it is important to have a variety of techniques to make them safe to share. A key concept within ATI legislation is the severability, or separability, of datasets.

It is still possible to sever columns containing personal data from a broader table and share the remainder. Severing datasets can be a useful way of rendering data safe for disclosure, but is not necessarily the most efficient approach to the mass disclosure of information given the time and effort needed to amend each dataset.

Similarly, documents that contain personal or sensitive personal information in text form can be redacted, obscuring or removing sections of text to render them compliant with any duty to protect personal or sensitive information. While redaction is a useful tool, it can be costly and time-consuming, requiring lawyers or trained specialists to trawl through what can be substantial amounts of documentation to remove personal and sensitive data. Although both severability and redaction have useful applications, they also have limitations as explained above and are unlikely to be useful approaches for the disclosure and sharing of large quantities of data on a routine basis.

De-identification techniques offer a more practical approach that may be more expensive and time consuming to set up initially, but may prove more efficient in the medium- and longer-term given that many can be automated within information systems.

De-identification is the process of removing data and information that can be used to identify individuals from datasets. A sub-set of de-identification includes data anonymization: the manipulation, or changing, of data to remove characteristics that make it harder to identify individuals.

Fed Access. Some EMR technology providers the owners of the enabling software platform may retain proprietary rights in that technology and so to an extent the records built on that platform Harty-Golder, An Act relative to patient health care information House Bill Designed to reduce the negative externalities imposed on data subjects in HIPAA transactions, it became obvious that the model was flawed in its applicability to emerging interoperable health record systems Terry and Francis, House, T. Woolley, M.

Protecting private sector good public model

Protecting private sector good public model

Protecting private sector good public model

Protecting private sector good public model

Protecting private sector good public model

Protecting private sector good public model. Content: Public Sector Vs Private Sector

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Public Private Partnerships (PPPs) for the Protection of Vulnerable Targets

How to effectively manage risk for the protection of facilities, technology assets and business operations. In late July, the U. Coincidentally, this year marks the twentieth anniversary of Presidential Decision Directive 63 PDD , the foundational executive branch guidance document on securing critical infrastructure. And significant thought and investment has taken place since on how to secure critical infrastructure. As early as , the Gramm—Leach—Bliley Act defined security expectations for protecting consumer banking information, and afforded banking regulators enforcement options if financial institutions do not establish and maintain adequate information security programs.

And yet, two decades later, despite significant policy attention, critical infrastructure risk has only grown. Last year, a number of major global companies were impacted by notPetya, a ransomware campaign that originated in Ukraine in June and has since been attributed to Russia.

Companies impacted by notPetya included pharmaceutical giant Merck, FedEx and Danish shipping giant Maersk — each to the tune of hundreds of millions of dollars. How do I know if I have an effective security program? Understanding effectiveness can be elusive, but it starts with a continuous cycle of enterprise-level security assessment, mitigation and monitoring of security risks.

Put another way, an effective security risk management strategy should, at a high level, a identify key risks particularly to high-value assets based on threat, vulnerability and potential consequence , b ensure that risk-based countermeasures — including people, process and technology — are designed and implemented to address those risks, and c measure and report on the effectiveness of these countermeasures.

Put another way, there is no shortage of guidance on how best to manage cyber risk, and yet many organizations struggle with both how to prioritize in the context of limited resources and changing risks, and how to measure progress. As clients build security risk management programs, we have found they trip up in six key areas:. In other words, security programs must factor the changing nature of inherent risk — i.

For example, U. As seen during the notPetya attacks, adversaries are using third-party software as a viable entry vector to deploy malware on targeted systems because security controls can be bypassed through the subversion of trusted third-party software. Malicious actors were able to infiltrate at the source of a supply chain, compromise the third-party software in question, and then leverage this compromise to inject malware into victim computer systems via a built-in auto-update process , which then spread laterally through those systems.

It is thus critical that organizations achieve strong visibility and management over software being developed, used and shared inside their IT environments and with customers. Changing customer and regulatory drivers e. Even where inherent risk is identified, programs may not succeed in prioritizing risk reduction capabilities appropriately.

Ineffective implementation sequencing can result in missed opportunities and meager security returns. This new information is used by investors and other stakeholders to re-assess their expectations of future behaviour and performance. As operational capabilities to defend the organization are implemented, these capabilities can quickly become overwhelmed by the sheer volume of data on potential threats and vulnerabilities.

Effective use of these tools is heavily dependent on risk-based prioritization — e. Cutting edge security tools are of limited use without increasing maturity in management of the underlying technology environment. Business leaders play a key role in advancing a security program.

IT organizations often depend on line-of-business leaders to provide necessary funding and address customer-related impacts associated with new security controls. It is thus critical that line-of-business leaders understand and prioritize security risks and resources into their business plans.

Business leaders also need to be prepared for their crucial role when addressing customers and the general public during a crisis situation. Indeed, major cyber incidents not only caused operational disruption, but also led to customer flight.

Thus, to be successful, a security program should — as a foundational matter — articulate how it protects the customer against adaptive threats. This articulation represents a key opportunity for senior management and the board to align business, security and technology executives on the vision for the enterprise-wide security program.

Controls without meaningful evaluation can decay over time, all-the-while affording a false sense of security. Likewise, a program must ultimately be measurable in some form to be managed.

And yet such metrics can be confusing to management e. In our experience, it can be helpful to view programs through several lenses including basic levels of visibility, risk-based hardening and vulnerability management trends, effectiveness against defined threat tactics, techniques and procedures, and business-centric security maturity.

These factors all suggest the need for more active private sector engagement in defining how the U. Of the above-cited private sector examples, all but one Uber have been either explicitly Merck, FedEx [13] or implicitly Equifax [14] tied to a hostile state actor. The U. National Security Agency that subsequently leaked.

We have seen a similar dynamic in the context of terrorism, whereby airlines, entertainment companies and other private-sector firms are basically pawns targeted by terrorist groups to achieve geopolitical objectives.

There are several steps the U. Moreover, while there is no such thing as risk elimination, the federal government can provide incentives to bolster defenses. Steve Daines R-Mont. We need investment from private-sector organizations in defending their own systems against these sorts of attacks.

Notwithstanding a rapidly increasing level of business, technology, threat and regulatory complexity, building an effective security program is both possible and necessary. Doing so requires continuous, disciplined private sector planning, enterprise-level alignment and focused effectiveness monitoring.

Given the nature of the threat facing critical infrastructure, it is also imperative that the U. Airlines, entertainment companies and other private-sector firms are basically pawns targeted by terrorist groups to achieve geopolitical objectives. Isles is tasked with leading and managing security risk management engagements, and oversee development of firm's security risk management methodology. Public, private and government sectors face a race against time in ensuring the critical infrastructure of the nation's technology, business and energy assets are secure.

Isles is tasked with leading and managing security risk management engagements and oversee development of firm's security risk management methodology. Adam Isles. September 20, Photo Courtesy of BigStock. Join Thousands of Fellow Followers Login or register now to gain instant access to the rest of this premium content! New product offerings, entry into new markets as well as merger and acquisition activity all entail risk implications for security programs, as do changes in foundational mechanisms for conducting business e.

It is vital that businesses have a process in place to assess risks associated with any major technology adaptation or change — such as cloud adoption. In fact, the Uber breach disclosed last year highlights this risk. Likewise, threat assessment is a foundational aspect of risk management — i. As part of this analysis, organizations must increasingly consider not just their own critical data and processes, but also related technologies e.

Planning Process Even where inherent risk is identified, programs may not succeed in prioritizing risk reduction capabilities appropriately. Using this approach, organizations can map out the lifecycle of an attack and align countermeasures to detect and block as early on in the lifecycle as possible. Ideally, security planning should not just reflect threats from external actors but also address insider threats. Moreover, security programs in many organizations are often bifurcated between physical and information assets rather than threat.

These stovepipes can obscure the detection of potentially important insider risk indicators. Likewise, insider threats by definition have some level of authorized access.

Detecting the misuse of authorized access involves a more nuanced capability set than detecting bright-line cases of unauthorized access, and thus requires focused planning. Since there is no such thing as risk elimination, resiliency the ability to withstand and recover from an attack becomes critical.

Therefore, it is imperative for management to have a firm view and understanding of the effectiveness of preparedness, as well as, response and recovery capabilities, for two reasons.

First, being prepared helps limit the extent of actual harm to the company — consider how ransomware can cause massive damage if not rapidly contained. Technology Dependencies Cutting edge security tools are of limited use without increasing maturity in management of the underlying technology environment. Stakeholder Alignment Business leaders play a key role in advancing a security program.

Monitoring for Effectiveness Controls without meaningful evaluation can decay over time, all-the-while affording a false sense of security. Engagement with the U. Government These factors all suggest the need for more active private sector engagement in defining how the U.

Conclusion Notwithstanding a rapidly increasing level of business, technology, threat and regulatory complexity, building an effective security program is both possible and necessary. Attributions: [1] Likewise, in the physical security domain, the benefits of globalization of travel, finance and communications also armed terrorist organizations with a more global reach for recruitment, financing and the operationalization of actual attacks.

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Protecting private sector good public model