Blend me in: Privacy-preserving input generalization for personalized online services

Baquero, Alegria; Schiffman, Allan; Shrager, Jeff

doi:10.1109/pst.2013.6596036

Cited by 2 publications

(3 citation statements)

References 33 publications

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“…One way to minimize the damage from data leakage is to not communicate it, or to distort it [2][3] [4]. In the present work we assume that the recipient requires the real data, as would almost certainly be the case for medical or financial transactions.…”

Section: A Control Of Data After It Is Transmittedmentioning

confidence: 99%

RAPPD: A Language and Prototype for Recipient-Accountable Private Personal Data

Kang

Schiffman²,

Shrager³

2014

2014 IEEE Security and Privacy Workshops

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Abstract-We often communicate private data in informal settings such as email, where we trust that the recipient shares our assumptions regarding the disposition of this data. Sometimes we informally express our desires in this regard, but there is no formal means in such settings to make our wishes explicit, nor to hold the recipient accountable. Here we describe a system and prototype implementation called Recipient-Accountable Private Personal Data, which lets the originator express his or her privacy desires regarding data transmitted in email, and provides some accountability. Our method only assumes that the recipient is reading the email online, and on an email reader that will execute HTML and JavaScript.

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Section: A Control Of Data After It Is Transmittedmentioning

confidence: 99%

RAPPD: A Language and Prototype for Recipient-Accountable Private Personal Data

Kang

Schiffman²,

Shrager³

2014

2014 IEEE Security and Privacy Workshops

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“…As for other applications in this area, PTTA access control is ensured by usernames and passwords, with the possible addition of unique IDs assigned by physicians, as mentioned above. Also, as suggested in [1], we will evaluate the extent to which exact values of Personal Identifiable Information (PII) are needed to provide useful targeted information. When the results do not depend upon exact values we will employ input generalization [1].…”

Section: Privacy and Securitymentioning

confidence: 99%

“…Also, as suggested in [1], we will evaluate the extent to which exact values of Personal Identifiable Information (PII) are needed to provide useful targeted information. When the results do not depend upon exact values we will employ input generalization [1]. We encrypt the PII with the Advanced Encryption Standard (AES) algorithm and store it separately from non-PII (literature-based data).…”

Section: Privacy and Securitymentioning

confidence: 99%

Adding Individual Patient Case Data to The Melanoma Targeted Therapy Advisor

Stevovic

Maxhuni

Khaghani-Far

et al. 2013

Proceedings of the ICTs for Improving Patients Rehabilitation Research Techniques

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The emergence of genomically targeted cancer treatments has spurred the development of methods that correlate genomic information with treatments and outcomes. Because this information is usually pulled from published literature, such methods are limited to summarizing only the data generated through the slow and narrow publication pipeline. However, many thousands of patients are treated each year whose data does not make it into publications. Each of these is, in effect, a case study whose capture would add to our overall knowledge of cancer treatment, and could speed up the search for treatments and cures. Our work extends one such literaturebased system, the Melanoma Targeted Therapy Advisor (TTA), by adding direct patient profiling. This extension of the TTA, or PTTA (Personalized or Patient TTA), both enriches the TTA knowledge base by collecting case reports directly from patients, and gives patients and/or physicians immediate feedback by ranking the best-performing treatments for genomic profiles of interest to them. The PTTA will permit patients to register their test and treatment results and then to obtain rankings for additional potentially useful tests and treatments. It will also provide a report with statistical and literature evidence that justifies the rankings. These functionalities can aid physicians in treating patients in the most effective manner.

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Blend me in: Privacy-preserving input generalization for personalized online services

Cited by 2 publications

References 33 publications

RAPPD: A Language and Prototype for Recipient-Accountable Private Personal Data

RAPPD: A Language and Prototype for Recipient-Accountable Private Personal Data

Adding Individual Patient Case Data to The Melanoma Targeted Therapy Advisor

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