Protecting Individual Privacy in the Struggle Against Terrorists A Framework for Program Assessment (2008) / Chapter Skim
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Appendix E: Hypothetical and Illustrative Applications of the Framework to Various Scenarios
Appendix E: Hypothetical and Illustrative Applications of the Framework to Various Scenarios
Pages 137-149
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From page 137... ...
NOTE: The committee emphasizes that the descriptions of technological approaches in this appendix are NOT an endorsement of or a recommendation for their use. E.1 AIRPORT SECURITY E.1.1 The Threat Terrorists continue to target air travel as an important objective.
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From page 138... ...
Computer-based machine learning algorithms could use such training data, collected from many security checkpoints at many airports, to formulate a potentially more accurate profile that could automatically estimate a risk level for each object seen in an x-ray scan and to assist the human screener with the goal of reducing the number of false alarms leading to invasive manual searches.
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From page 139... ...
Anonymous images of baggage, even if stored for future data mining, might be perceived as less invasive than baggage images associated with the owner. • Coertness of collection.
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From page 140... ...
Effectiveness First, the framework asks for a clearly articulated purpose for the new system, an evaluation of why it may out perform current methods, and a thorough experimental evaluation of the system before full deployment. Note one might experimentally evaluate whether the data mining software of company X is capable of distinguishing home-made versus mass-produced luggage without going to the step of a full network deployment, by testing its use in one or two individual trial airports first.
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From page 141... ...
Does this agency have a policy-level privacy officer, are its employees and others who might access the data trained appropriately, and are all of the uses of this nationwide luggage image dataset clearly articulated and in compliance with existing laws? E.2 SYNDROMIC SURVEILLANCE E.2.1 The Threat A major issue for those concerned with ensuring public health is the early detection of an outbreak or attack capable of causing widespread disease, injury, or death.
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From page 142... ...
These forms of so-called "syndromic surveillance" are geared toward achieving the earliest possible detection of public health emergencies.1 Syndromic surveillance requires access to many different kinds of data. For example, in a large city, the data streams into a syndromic surveillance system might include digital records of common, OTC sales of medicines from pharmacies in the city, absentee records from city schools and some select businesses, counts of 911 calls to the city categorized into more than 50 call types (e.g., "influenza like illness," "breathing problems," and so on)
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From page 143... ...
APPENDIX E BOX E.1 An Illustrative Operational Scenario for the Use of Syndromic Surveillance On a winter afternoon, a GoodCity public health official conducting routine daily data analysis notes a spike in the number of hospital emergency department (ED) visits and pharmacy sales detected by GoodCity's syyndromic surveillance system, which is designed to detect early, indirect indicators of a possible bioterror attack.
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From page 144... ...
Rather than simply analyzing these data streams separately and noting temporal correlations in them, considerably more inferential power would be available if it were possible to associate a specific child absent from school on Tuesday with the purchase of cough syrup on Tuesday by his father. However, linking attendance records to drug store purchasing records in such a manner would require personal identifiers in each stream to enable such a match.
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From page 145... ...
There is good evidence that syndromic surveillance systems can detect large disease outbreaks, but it is less clear how and if such detection improves public health response. Health officials confronted with a spike in syndromic signals typically seek more definitive evidence of a true rise in illnesses among city residents before taking action.3 This is in part because there is a lot of noise in the systems -- illness rates, OTC medicine purchases, 911 reports -- that varies widely even within a given season and location.
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From page 146... ...
Observable behaviors that might precede patients seeking medical care for an illness are not precisely known. Although in 1993 a run on OTC medicines in Milwaukee famously preceded public health detection of a large, waterborne cryptosporidiosis outbreak, the purchase of nonprescription, OTC medicines does not reliably precede outbreaks of illness in populations.7 Moreover, a retrospective analysis of 3 years of syndromic surveillance data gathered by the New York City Health Department concluded that "syndromic surveillance signals [for gastrointestinal disease outbreaks]
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From page 147... ...
The complex and larger data streams are also likely to increase the complexity of the investigations that follow the detection of syndromic "signals," which could further delay any response action.10 There are also great difficulties in doing real-time record linkage on multiple data streams. With static record linkage, all of the databases in question are available for analysis, which means that it is possible to perform cross-validation, error assessment, and careful blocking to reduce comparisons.
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From page 148... ...
current syndromic systems the data are anonymized before being sent to public health agencies. In many published articles on syndromic surveillance, the emergency room data constitute the most important and useful data stream for both detecting and ruling out disease outbreaks.
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From page 149... ...
Frequency of false positives is a major concern with these systems, as the scenario in Box E.1 demonstrates. In large public health agencies where resources exist to maintain and staff syndromic surveillance systems appropriately and where digitized data streams are available, such systems may be cost-effective.
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