Vision PaperEnabling Privacy for the Paranoids

AGGARWAL, G; BAWA, M; GANESAN, P; GARCIAMOLINA, H; KENTHAPADI, K; MISHRA, N; MOTWANI, R; -, Uddeshya Srivastava; THOMAS, D; WIDOM, J

doi:10.1016/b978-012088469-8/50063-2

Cited by 23 publications

(37 citation statements)

References 23 publications

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“…Then, it sends the hashed list to data source P j so that it can compare the incoming hashed list with its own hashed list to find the common items in L i and L j . According to this scheme, in Example 1, data source P 1 In the context of data warehouses, data sources usually have more information about the secret items. For example, all data sources in Example 1 keep name, last name, phone and address information as well as SSN (Social Security Number) of a customer in their customers tables.…”

Section: Aggregated Intersectionmentioning

confidence: 99%

“…There have been several techniques in the areas of database and cryptography for privacy preserving data sharing. One of them is to use trusted third parties such that data sources hand over their data and a third party performs the computation on their behalf [1,10]. The level of trust may not be acceptable in these methods.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, hotel H 1 cannot send its data to airlines so that airlines can extract the information that hotel H 1 needs since airlines will discover each other's customers. In order to be able to collaborate, hotel H 1 should take its customers' information from airlines in a way that airlines A 1 and A 2 share their data with hotel H 1 by only revealing common customers (i.e., revealing H 1 ∩ A 1 to H 1 and A 1 and H 1 ∩ A 2 to H 1 and A 2 ). By using such a method hotel H 1 cannot discover new customers which may be customers of hotel H 2 and also airlines cannot discover new customers which may be customers of the other airline.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ABACUS: A Distributed Middleware for Privacy Preserving Data Sharing Across Private Data Warehouses

2005

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Abstract. Recent trends in the global economy force competitive enterprises to collaborate with each other to analyze markets in a better way and make decisions based on that. Therefore, they might want to share their data with each other to run data mining algorithms over the union of their data to get more accurate and representative results. During this process they do not want to reveal their data to each other due to the legal issues and competition. However, current systems do not consider privacy preservation in data sharing across private data sources. To satisfy this requirement, we propose a distributed middleware, ABA-CUS, to perform intersection, join, and aggregation queries over multiple private data warehouses in a privacy preserving manner. Our analytical evaluations show that ABACUS is efficient and scalable.

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Section: Aggregated Intersectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ABACUS: A Distributed Middleware for Privacy Preserving Data Sharing Across Private Data Warehouses

2005

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“…Regulation of data collection for Web users is first considered by W3C through the privacy specification standard called the Platform for Privacy Preferences (P3P1.0) [5]. Subsequent research criticizes P3P because it does not guarantee Web sites have the means to implement and respect their privacy policies ( [2], [6], [8]). Social Contract Core (SCC) [10] extends P3P by allowing users to choose privacy preferences that suit them.…”

Section: Related Workmentioning

confidence: 99%

“…delete records older than 3 years) and a Policy Evaluation Engine to control user access to personally identifiable information. The Paranoid Platform for Privacy Preferences (P4P) [2] envisions a world where personal agents help individuals to control the destination, type, scope and duration of use of released personal information. Our work considers XML data collections where each query has a purpose assigned as in Hippocratic databases [4].…”

Section: Related Workmentioning

confidence: 99%

P4A: A New Privacy Model for XML

Duta

Barker

2008

Lecture Notes in Computer Science

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Abstract. We propose a new privacy model for XML data called Privacy for All (P4A) to capture collectors privacy practice and data providers privacy preferences. Through P4A data collectors specify the purpose of data collection along with recipients, retention time and users. Data providers can agree to the collectors' practice or impose their own privacy preferences. P4A offers more flexibility to both data collectors and providers in specifying privacy statements and preferences, including but not limited to full permission, denial, and conditional access to information.A privacy practice defines purposes, recipients, retention period, and uses of data collection. Data providers share their private information with data collectors under restrictions specified by privacy preferences. P4A offers individuals multiple options for restrictions such as conditional access, return results as range intervals for each data item and purpose.

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Privacy Preserving in Location Based Services

Movahedi

Zamani

2017

Encyclopedia of GIS

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SynonymsDistributed information systems; Epidemics; Epidemiology, computational; Information management systems; Public health DefinitionEpidemiology is the study of patterns of health in a population and the factors that contribute to these patterns. It plays an essential role in public health through the elucidation of the processes that lead to ill health as well as the evaluation of strategies designed to promote good health. Epidemiologists are primarily concerned with public health data, which includes the design of studies, evaluation and interpretation of public health data, and the maintenance of data collection systems.Computational Epidemiology is the development and use of computer models for the spatio-temporal diffusion of disease through populations. The models may range from descriptive, e.g. static estimates of correlations within large databases, to generative, e.g. computing the spread of disease via person-toperson interactions through a large population. The disease may represent an actual infectious disease, or it may represent a more general reaction-diffusion process, such as the diffusion of innovation. The populations of interest depend on the disease, including humans, animals, plants, and computers. Similarly, the interactions that must be represented depend on the disease and the populations, including physical proximity for aerosol-borne disease, sexual contact for sexually transmitted diseases, and insect feeding patterns for mosquito-borne diseases. In general, then, computational epidemiology creates computer models of diffusive processes spreading across interaction networks.The basic goal of epidemiological modeling is to understand the dynamics of disease spread well enough to control it. Potential interventions for controlling infectious disease include pharmaceuticals for treatment or prophylaxis, social interventions designed to change transmission rates between individuals, physical barriers to transmission, and eradication of vectors. Efficient use of these interventions requires targeting sub- populations that are on the critical path of disease spread. Computational models can be used to identify those critical subpopulations and to assess the feasibility and effectiveness of proposed interventions. Historical BackgroundEpidemiology did not emerge as a distinct discipline until the mid-nineteenth century as the medical sciences sought to determine the efficacy of different medical practices. John Snow famously interrupted the 1854 cholera outbreak in London by removing the handle of the Broad Street pump, an event that is widely credited with bringing epidemiology into the mainstream. His studies along with those of many others were responsible for bringing about wide-ranging public health reforms and laid the foundation for the development of the germ theory of disease causation. Once etiological agents were identified as the cause of disease, the sanitary reforms of the late nineteenth and early twentieth centuries greatly reduced the incidence of infectious disease i...

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Vision PaperEnabling Privacy for the Paranoids

Cited by 23 publications

References 23 publications

ABACUS: A Distributed Middleware for Privacy Preserving Data Sharing Across Private Data Warehouses

ABACUS: A Distributed Middleware for Privacy Preserving Data Sharing Across Private Data Warehouses

P4A: A New Privacy Model for XML

Privacy Preserving in Location Based Services

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