MOTIVATION

There are numerous real-world applications which require data integration while meeting specific privacy constraints. The following, discusses, some of the “motivating drivers”.
1. Sharing Scientific Research Data
Analyzing the prevalence, incidence, and risk factors of diseases is crucial
to understanding and treating them. Such analyses have significant impact on policy decisions. An obvious pre-requisite to (carrying out) such studies is to have the requisite data available. First, data needs to be collected from disparate health care providers and integrated while sanitizing privacy-sensitive information.
This process is extremely time consuming and labour intensive. . A breach of privacy can lead to significant damage (harm) to individuals both materially and/or emotionally. Another problem is the possibility of discrimination against various sub-groups from seemingly conclusive statistical results. Similarly, health care providers themselves risk loss by leaking accurate data reflecting their performance and weaknesses.

Privacy is addressed today by preventing dissemination
rather than integrating privacy constraints into the data sharing process. Privacy-preserving integration and sharing of research data in health sciences has become crucial to enabling scientific discovery.


2. Effective Public Safety
Integration and sharing between public agencies, and public and private organizations, can have a strong positive impact on public safety. But concerns over the privacy implications of such private/public sector sharing have
impacted uses of data mining in public safety:
For example, fire fighting departments in Illinois routinely seek sample regulations and training materials from fellow fire fighting departments (e.g., handling a bio-hazard situation, or an unknown emerging public safety threat). Such materials allow them to develop similar programs and to provide the most up-to-date effective community defense. However, fellow departments are reluctant to share such materials, for fear of liability if programs are deemed inadequate. They would be happy to share the material if identity (and thus liability exposure) was protected.

3. Monitoring Healthcare Crisis
Detecting and containing disease outbreaks early is key to preventing life-threatening infectious diseases, witness the successful eradication of smallpox. Outbreaks of infectious diseases such as West Nile, SARS, and bird flu; as well as threats of bio-terrorism; have made disease surveillance into a national priority. Outbreak detection works best when a variety of data sources (human health-care, animal health, consumer data) are integrated and evaluated in real time. For example, the Real-Time Outbreak Detection System (at the University of Pittsburgh Medical Center) uses data collected from regional healthcare providers and purchase records of over-the-counter drugs to determine outbreak patterns. This system forwards all regional data to a central data warehouse for evaluation purposes.
Privacy laws typically do not cover government public health organizations, raising the spectre of systems with inadequate privacy protection. The concerns are similar to the risks noted above for healthcare research data: External attacks or insider misuse can damage individuals, healthcare providers, or groups within society. Protecting identity and liability exposure by effective privacy-preserving data integration and sharing techniques will enable advances in emergency preparedness and response, public safety, health care and homeland security that might otherwise be prevented due to privacy concerns.

4 Facilitating Ecommerce
There are innumerable opportunities in e-commerce to enable beneficial collaboration, if privacy concerns could be met. Corporation will not (in some cases, cannot) share confidential data with each other, but are willing to engage in some process for mutual benefit. As an example, consider secure supply-chain management. An example scenario would be two companies that use a common raw material. Knowing that they share this need and coordinating their orders and production would enable smoothing out the supply line and improving overall supply chain efficiency. A prerequisite for this coordination is the ability to identify the common raw material, suppliers, customers, etc., without giving up competitive knowledge advantages or violating anti-trust law. Standards for sharing logistics information cover such a wide ground that ambiguity is inevitable (e.g., the ECCMA Open Technical Dictionary has over 30,000 standard attribute names).