k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY

Author(s):  
LATANYA SWEENEY

Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version of its private data with scientific guarantees that the individuals who are the subjects of the data cannot be re-identified while the data remain practically useful? The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment. A release provides k-anonymity protection if the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release. This paper also examines re-identification attacks that can be realized on releases that adhere to k-anonymity unless accompanying policies are respected. The k-anonymity protection model is important because it forms the basis on which the real-world systems known as Datafly, μ-Argus and k-Similar provide guarantees of privacy protection.

2005 ◽  
Vol 01 (03) ◽  
pp. 373-392 ◽  
Author(s):  
YUKIO OHSAWA

This paper introduces the concept of chance discovery, i.e. discovery of an event significant for decision making. Then, this paper also presents a current research project on data crystallization, which is an extension of chance discovery. The need for data crystallization is that only the observable part of the real world can be stored in data. For such scattered, i.e. incomplete and ill-structured data, data crystallizing aims at presenting the hidden structure among events including unobservable ones. This is realized with a tool which inserts dummy items, corresponding to unobservable but significant events, to the given data on past events. The existence of these unobservable events and their relations with other events are visualized with KeyGraph, showing events by nodes and their relations by links, on the data with inserted dummy items. This visualization is iterated with gradually increasing the number of links in the graph. This process is similar to the crystallization of snow with gradual decrease in the air temperature. For tuning the granularity level of structure to be visualized, this tool is integrated with human's process of chance discovery. This basic method is expected to be applicable for various real world domains where chance-discovery methods have been applied.


2020 ◽  
Vol 2 (3) ◽  
pp. 233-255
Author(s):  
Mario Manzo

In the real world, structured data are increasingly represented by graphs. In general, the applications concern the most varied fields, and the data need to be represented in terms of local and spatial connections. In this scenario, the goal is to provide a structure for the representation of a digital image, called the Attributed Relational SIFT-based Regions Graph (ARSRG), previously introduced. ARSRG has not been described in detail, and for this purpose, it is important to explore unknown aspects. In this regard, the goal is twofold: first, to provide a basic theory, which presents formal definitions, not yet specified above, clarifying its structural configuration; second, experimental, which provides key elements about adaptability and flexibility to different applications. The combination of the theoretical and experimental vision highlights how the ARSRG is adaptable to the representation of the images including various contents.


Author(s):  
LATANYA SWEENEY

Often a data holder, such as a hospital or bank, needs to share person-specific records in such a way that the identities of the individuals who are the subjects of the data cannot be determined. One way to achieve this is to have the released records adhere to k-anonymity, which means each released record has at least (k-1) other records in the release whose values are indistinct over those fields that appear in external data. So, k-anonymity provides privacy protection by guaranteeing that each released record will relate to at least k individuals even if the records are directly linked to external information. This paper provides a formal presentation of combining generalization and suppression to achieve k-anonymity. Generalization involves replacing (or recoding) a value with a less specific but semantically consistent value. Suppression involves not releasing a value at all. The Preferred Minimal Generalization Algorithm (MinGen), which is a theoretical algorithm presented herein, combines these techniques to provide k-anonymity protection with minimal distortion. The real-world algorithms Datafly and μ-Argus are compared to MinGen. Both Datafly and μ-Argus use heuristics to make approximations, and so, they do not always yield optimal results. It is shown that Datafly can over distort data and μ-Argus can additionally fail to provide adequate protection.


2022 ◽  
Vol 16 (1) ◽  
pp. 0-0

Privacy protection is a hot topic in network security, many scholars are committed to evaluating privacy information disclosure by quantifying privacy, thereby protecting privacy and preventing telecommunications fraud. However, in the process of quantitative privacy, few people consider the reasoning relationship between privacy information, which leads to the underestimation of privacy disclosure and privacy disclosure caused by malicious reasoning. This paper completes an experiment on privacy information disclosure in the real world based on WordNet ontology .According to a privacy measurement algorithm, this experiment calculates the privacy disclosure of public figures in different fields, and conducts horizontal and vertical analysis to obtain different privacy disclosure characteristics. The experiment not only shows the situation of privacy disclosure, but also gives suggestions and method to reduce privacy disclosure.


1993 ◽  
Vol 9 (2) ◽  
Author(s):  
Robert M. Corderoy ◽  
Barry M. Harper ◽  
John G. Hedberg

<span>Many of the software packages presently marketed as simulations are in fact little more than 'pre-set', limited models of the 'real world' systems they are designed to emulate. There is little scope for the user to interact with the model as they would in a 'real world' experience, and this must compromise the intended educational outcomes. The exact nature of what constitutes a 'good simulation' is not agreed upon among researchers or designers alike, but if the goal of the simulation is to provide experiences which approach those in the 'real world', and in so doing, provide opportunity for the development of higher order skills which research in cognitive science is suggesting are important, one must strive for the greatest degree of user interaction as possible.</span><p>The achievement of this goal rests with the application of HyperMedia based platforms which may be exploited for their ability to provide the 'genuine interaction' essential to 'real world' systems, across the computer-user boundary. A number of design issues must be addressed if the full potential of HyperMedia based platforms is to be harnessed.</p>


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1967-1967
Author(s):  
Sikander Ailawadhi ◽  
Ching-Kun Wang ◽  
Andrew J. Belli ◽  
Catarina Jansson Blixt ◽  
Diana Cripps ◽  
...  

Abstract Introduction: Melphalan flufenamide (melflufen) is a first-in-class peptide-drug conjugate that targets aminopeptidases and rapidly releases alkylating agents into tumor cells. The single-arm, open-label, Phase 2 HORIZON study (NCT02963493), conducted in Europe and the USA, demonstrated in patients with heavily pre-treated relapsed/refractory multiple myeloma (RRMM), including those with triple-class-refractory and extramedullary disease, that melflufen in combination with dexamethasone showed clinically meaningful efficacy and a manageable safety profile (Richardson PG, et al. J Clin Oncol. 2021;39:757-767). Comparative trials between all regimens/agents are not feasible, and hence, real-world datasets may be valuable to allow the comparison of patient outcomes in single-arm clinical trials with similar patient populations in the real world. The objective of the REAL-world Myeloma (REALM) study was to compare clinical outcomes between patients in the COTA database and those of patients in the HORIZON study in order to compare routine care with melflufen. Methods: REALM is a retrospective, observational study using real-world data from 605 patients with RRMM collected in the COTA database, a USA-based real-world evidence database comprising longitudinal, Health Insurance Portability and Accountability Act (HIPAA)-compliant data on the diagnosis, clinical management, and outcomes of patients with cancer. Patients in REALM met the main HORIZON eligibility criteria (aged ≥18 years; ≥2 lines of prior therapy [including an immunomodulatory drug and a proteasome inhibitor]; refractory to pomalidomide and/or daratumumab on or after January 1, 2015; and received a line of therapy after meeting the inclusion criteria). Propensity score (PS) matching is a commonly used approach for comparing the effectiveness of 2 treatment options across different studies where individual patient data are available for both treatments. This abstract presents adjusted clinical outcome results based on 1:1 PS matching of patients in the COTA and HORIZON datasets for 12 covariates (COTA PS and HORIZON PS, Table 1). These covariates were considered important because they reflect patient characteristics that impact clinical outcomes and were validated in a feasibility study, and by independent experts. The primary endpoint of the HORIZON study was overall response rate. The primary endpoints of the REALM study were time to treatment discontinuation (TTD) and time to next treatment (TTNT), to mitigate against missing treatment response data in the COTA database identified in the feasibility study. Results: Selected patient demographic and clinical characteristics were generally well matched (Table 1) between patients from COTA PS (n=110) and HORIZON PS (n=110), except for the Eastern Cooperative Oncology Group performance status, which was better in the COTA PS dataset than in the HORIZON PS dataset. Patients in both the COTA PS and the HORIZON PS datasets were predominantly male and White. Patients had a median age of 67 years in the COTA PS dataset and 64 years in the HORIZON PS dataset, with a median of 4 and 5 prior lines of therapy, respectively. The median time since first diagnosis to index data was 60.19 months in the COTA PS dataset and 72.56 months in the HORIZON PS dataset. The median TTNT (95% confidence interval [CI]) in the COTA PS dataset versus the HORIZON PS dataset was 4.6 (3.7-6.9) months and 5.9 (4.7-7.7) months, respectively (p=0.949). The median TTD (95% CI) in the COTA PS dataset versus the HORIZON PS dataset was 3.2 (2.6-3.9) months and 3.9 (3.0-4.6) months, respectively (p=0.642). Conclusions: Pharmacoepidemiologic methods using individual patient data play an important role in evaluating the effectiveness of treatments in the absence of randomized controlled trial data. Patients with RRMM receiving melflufen in combination with dexamethasone noted a trend toward improved TTNT and TTD compared with corresponding real-world treatment approaches. This benefit may be clinically meaningful especially in the triple-class-refractory patient population, which remains an unmet need in RRMM. Figure 1 Figure 1. Disclosures Ailawadhi: Karyopharm: Consultancy; AbbVie: Consultancy; Genentech: Consultancy; Takeda: Consultancy; GSK: Consultancy, Research Funding; Xencor: Research Funding; Cellectar: Research Funding; Medimmune: Research Funding; Ascentage: Research Funding; Pharmacyclics: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Bristol Myers Squibb: Consultancy, Research Funding; BeiGene, Ltd.: Consultancy; Sanofi: Consultancy; Oncopeptides: Consultancy. Wang: COTA, Inc.: Current Employment, Other: Equity ownership. Belli: COTA, Inc.: Current Employment, Other: Equity ownership. Jansson Blixt: Oncopeptides: Current Employment, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company. Cripps: Oncopeptides: Other: Independent consultant. Zavisic: Oncopeptides: Current Employment, Current holder of stock options in a privately-held company. Ramasamy: Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Conference registration, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Conference registration, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Conference registration, Research Funding; Celgene (BMS): Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Conference registration, Research Funding; GSK: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive biotech: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees.


2010 ◽  
Vol 20 (3) ◽  
pp. 100-105 ◽  
Author(s):  
Anne K. Bothe

This article presents some streamlined and intentionally oversimplified ideas about educating future communication disorders professionals to use some of the most basic principles of evidence-based practice. Working from a popular five-step approach, modifications are suggested that may make the ideas more accessible, and therefore more useful, for university faculty, other supervisors, and future professionals in speech-language pathology, audiology, and related fields.


2006 ◽  
Vol 40 (7) ◽  
pp. 47
Author(s):  
LEE SAVIO BEERS
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document