The purpose of comparative effectiveness research (CER) is to inform patients, providers, and decision makers about the effectiveness of different interventions.1 CER promises enormous societal benefits by promoting new scientific evidence in medicine, speeding up clinical discoveries, and enabling cost-effective and time-effective patient care. To achieve these goals, CER researchers must obtain access to a wide range of information (eg, demographics, laboratory tests, genomic data, and outcomes) from a variety of population groups. Institutions face fundamental challenges in how to share data with researchers or with the public; they must balance the privacy of patient data with the benefits of CER.
The Privacy Rule of the Health Insurance Portability and Accountability Act (HIPAA) sets standards for the privacy and security of health records in the United States.2 HIPAA defines 2 approaches for deidentification: expert determination and safe harbor. The expert determination approach requires that a statistician certify that the reidentification risk in the data is “sufficiently low.” The safe harbor approach, in contrast, explicitly requires the removal and suppression of a list of attributes.3 The Department of Health and Human Service has recently issued revised guidance on methods for deidentifying protected health information (http://www.hhs.gov/ocr/privacy/hipaa/understanding/coveredentities/De-identification/guidance.html). These changes clarify the deidentification standard and how to perform deidentification, but do not change the existing standards. For the expert determination approach, the new guidance defines key concepts such as covered entities, business associates, and acceptable risk, explains standards for satisfying the standard, and gives examples of how expert determination has been applied outside the health care context, such as within government statistical agencies like the Bureau of the Census. For the safe harbor approach, the guidance provides more examples, including when zip codes and elements of date can be preserved in the deidentified data, and how to use data use agreements when sharing deidentified data. These statutory requirements are included in the Appendix, http://links.lww.com/MLR/A509.
There are numerous controversies on both sides of the privacy debate regarding these HIPAA privacy rules.4 Some people believe that protections in the deidentified data under HIPAA are not sufficient5—a 2005 national consumer health privacy survey also showed that 67% of national respondents remain concerned about the privacy of their personal health information,6 indicating a lack of public trust in the protection offered by HIPAA deidentified data.7 In contrast, others contend too many privacy safeguards hamper biomedical research, and implementing these safeguards precludes meaningful studies of medical data that depend on suppressed attributes (eg, epidemiology studies in low-population areas or geriatric studies requiring detailed ages over 893). They also worried about the harm caused by privacy rules—they could erode the efficiencies offered by computerized health records and possibly interfere with law enforcement.4
In practice, privacy always comes with a loss of utility—perfect privacy is only possible when no data are shared. However, this measure of utility is application dependent. In this paper, we focus on data-sharing problems that may arise in CER applications. For such applications, we can measure utility by metrics such as classification accuracy and/or calibration. Some privacy-preserving operations may destroy too much information to achieve these target goals. Privacy metrics allow institutions to evaluate the tradeoff between the improvements from integrating additional data and the privacy guarantees from the privacy-preserving operations.
To realize the benefits of improved care through CER, data holders must share data in a way that is sensitive to the privacy concerns of patients. We synthesize and categorize the state-of-the-art in privacy-preserving data sharing, a topic that has sparked much research in the last decade. We expect this study can guide CER researchers in choosing a privacy method, inform institutions in developing data-sharing agreements, and suggest new directions for privacy researchers.
MATERIALS AND METHODS
We adapted a systematic review methodology, suggested by Centre for Reviews & Dissemination guide,8 to review the research literature. Figure 1 illustrates our flow of information through the different phases of this review process. We chose to query 7 databases, shown in Table 1. We used the basic format of posing broad queries to capture as many relevant articles as possible and then applied targeted inclusion criteria to find those works relevant to CER. The search included documents published up to February 1, 2012. The online Supplemental digital content of this manuscript provides details of our methods. Because many relevant studies were identified in the computer science (CS) literature, we provide some explanation for CS-specific terminology in Table 2 for the benefit of readers.
The details of our study are available in Supplemental digital content. To contextualize our findings for privacy-preserving data techniques, we outline 3 examples of typical situations that may arise in the context of CER.
Researchers from Institution A want to study the benefits of minimally invasive surgery of their own patients and patients at Institution B, another hospital that routinely use Da Vinci Robotic Surgical system to conduct minimally invasive surgery for cardiac patients. To provide information about their patients, Institution B generates an anonymized data table, together with a data-use agreement limiting access to authorized researchers at Institution A.
Institution A wants to make collected data about diabetes care available to researchers (internal or external), who study diabetes complications in stroke. Instead of sharing data directly with individual researchers, Institution A sets up a hosted data warehouse to answer the queries of researchers through a secure web interface (eg, clinical data warehouse).
Institution A wants to make collected readmission rates of cardiac patients (within 30 d of discharge) publically available for the purpose of safety surveillance. Statisticians at the Institution A analyze the raw data and generate a number of statistical analyses, summaries, and tables derived from the data to be published.
In the CER context, the above-mentioned examples represent different modalities for data sharing. When data are shared directly between institutions, they are covered by a data use agreement. In this scenario, the major challenge is protecting the data confidentiality during the transfer process. Regarding the clinical data warehouse scenario, data stewards implement a controlled interface to the sensitive data so that the answers to the queries are protected (similar to those existing ones like i2b29 and CRIQueT10). For the public dissemination case in our last example, the type of data that can be released is much more limited than in controlled settings (eg, no individual patient records). It is important to choose appropriate anonymization models, techniques, and algorithm parameters in conjunction with data use agreements to avoid information breaches.
General Metrics and Methods
The word “privacy” has different meanings depending on the context. What we refer to as “privacy” in this paper often goes by “confidentiality” in the statistical literature.11,12 The goal of privacy-preserving data sharing is to manipulate the original data in such a way as to prevent reidentification of identities or sensitive attributes. There are many methods for publishing versions of the original data, such as suppression of unique elements, top/bottom coding to limit ranges of values, generalization by merging categories, rounding values to limit uniqueness, and adding noise. Another approach is to simply release synthetic data that in some way “looks like” the real data—methods for this include sampling and partial data substitution. If releasing the original data is not necessary or is considered too risky, summary statistics or subtables of the data can be generated from the original data. More complex anonymization and sanitization algorithms are often built on these basic operations using structural properties of the datasets. Fayyoumi et al13 reviewed various techniques on statistical disclosure control and microaggregation techniques for secure statistical databases.
Altering the original data makes the disclosed data less useful, so a key element of privacy technologies is providing a metric for the level of protection.14–16 This allows empirical evaluations of the difference in utility between the original and manipulated data. There are many surveys of privacy operations and metrics,12,17–20 but they do not address applications in CER.
The choice of a privacy model or technology depends on the perceived threats to confidentiality; it is therefore important to specify to whom the data are being shared and what sort of external restrictions are placed on the recipients of the data. Many proposed methods for privacy-preserving data analysis or sharing do not provide any formal or quantifiable guarantees of privacy; instead, they claim that because the shared data are sufficiently “different” from the original data, they are inherently private. A useful privacy-preserving data sharing method should specify the threats and quantify the level of protection provided. Quantification of the privacy risk is important because it allows the system designer to compare different algorithms and evaluate the tradeoffs between privacy and the utility of sanitized data.19
We can divide the privacy metrics proposed in the literature into 2 categories: syntactic and semantic. Syntactic metrics are defined in terms of properties of the postprocessed “sanitized” data. For example, k-anonymity21 guarantees that, for any combination of feature values, if there is 1 person with those features, there are at least k with the same feature values. To achieve this goal, original feature values may be merged (eg, laboratory tests are reported as ranges rather than values). The anonymization system Datafly22 uses k-anonymity, and many government agencies use a “rule of k” (another version of k-anonymity) to determine if data are anonymized. Other metrics such as l-diversity23 and t-closeness,24 or m-invariance25 provide related guarantees on the level of masking. There is extensive literature about attacks on these privacy models.26–31
Semantic privacy measures are defined in terms of the properties of the process of data sanitization. The most studied version of semantic privacy is differential privacy,23 which provides a statistical guarantee on the uncertainty in inferring specific values in the data. In syntactic privacy, the released dataset satisfies particular privacy conditions, whereas in semantic privacy, the process guarantees privacy, regardless of the underlying data. However, differential privacy is still subject to inferential attacks.32 Another model for privacy risks is ∂-presence,33,34 which models the effect of public data on inferring the presence of individuals in a dataset.
Regarding assumptions on threats, syntactic privacy methods either assume that the recipient of the data knows nothing about the individuals in the data or assume that the adversaries have limited knowledge. The former is a dangerous assumption, especially for public release datasets, as there are many publically available datasets that can be used to launch a so-called linkage attack (Table 2). The second approach is difficult because it requires modeling the knowledge of an unknown adversary. In contrast, differential privacy guarantees that an adversary with full knowledge of all but 1 individual’s data will still have difficulty inferring the data of that individual. Although it is a robust definition in this sense, many differentially private algorithms are not practical for use on small-sized or moderate-sized datasets.35
These models of adversarial knowledge are pessimistic in that they assume the recipient of the data intends to reidentify individuals. Although this may be a reasonable assumption for some users of public-use datasets such as Medicare billing data, in other scenarios the data holder can issue enforceable data use policies that can hinder reidentification attempts. Prohibiting reidentification research is a mistake that can prove very costly in the near future, but developing -accompanying policies limiting access to sensitive data through data-sharing agreements or hosted enclaves can reduce the chance of inadvertent identity disclosure.
Identified Privacy Methods for CER Applications
In Institution-to-institution Sharing
The privacy risks are not as uncontrolled as they are in public data release. We found several articles in the literature that suggest algorithms to address this kind of data-sharing scenario. The kind of protections provided and the resulting utility of the data are different for these methods. There is an extensive literature on k-anonymizing21 a dataset before publication or sharing,36–44 and there are also implementations satisfying other syntactic privacy measures.43–51 A k-anonymization approach was proposed by El Emam et al52 in the context of (public) data publishing for medical data. Another recent promising approach for linking data sources in an federated system was proposed by Mohammed et al.53 The effect of these approaches on utility can vary. Some work has focused on enhancing utility through postprocessing54 or evaluating the effect of anonymization on specific statistical tasks.55
Perturbing the data table before information exchange can also protect privacy. The perturbation can be chosen to provide a privacy guarantee or to maintain a certain level of utility. Some of this work arose from the statistical literature and used statistical measures for measuring privacy, such as posterior odds56 or other metrics.57 In contrast with the syntactic investigations of k-anonymity, noise addition has a more directly measurable impact on utility, and several studies investigated the effect of the noise on the utility of the data. Differential privacy has been proposed for sharing anonymized tables of data such as contingency tables.58 More advanced methods with utility analyses for differential privacy have been developed using wavelet transforms.59
Secure multiparty computation allows multiple parties to perform computation on their private data to evaluate some function of their common interest.60,61 Basically, these approaches apply a set of cryptography-motivated techniques to ensure that data sources collaborate to obtain results without revealing anything except those results.62 Secure multiparty computation techniques have been developed for classification,63,64 clustering,65 association rule mining,66 and data disclosing for disease surveillance,67 which demonstrated powerful privacy protections. A detailed classification of these algorithms was provided by Xu and Yi.68 A recent paper69 suggested privacy and collaborative data mining (ie, CER data mining) can be achieved at the same time when the computational task is well defined.
In an Institution-hosted Framework
CER researchers have access to the data through an interactive mechanism that can monitor and track their privacy usage. This is a preferable arrangement when the information that needs to be shared is not known in advance or may change over time. Although queries can be processed on a special anonymized dataset created using the techniques mentioned in the previous section,70 there are some approaches to explicitly handle interactive queries. Syntactic privacy methods generally do not address interactive methods, although recent work has reported on a framework for instant anonymization.71
Differential privacy was first proposed in the context of interactive queries.72–75 Typically, privacy is enforced through returning noisy responses to queries, although theoretical work has proposed more complex query processing.76 This privacy model has been incorporated into query languages for data access77,78 and MapReduce, which is the system used by Google and others to perform computations on large datasets.79 In the medical informatics community, noise addition has been proposed for exploratory analysis in a clinical data warehouse9 and differential privacy has been proposed for count queries.57 Other approaches to online analytical processing use statistical measures of privacy.45
To Prepare Data for Public Release
The data custodians need to set the confidential level high enough to protect sensitive patient privacy from breaches because a broad disclosure of health data poses a much more significant privacy breach risk than previous scenarios of institution-to-institution and institution-hosted data access. Recent examples stemming from data shared by Netflix and AOL showed that simply removing identifiers or naive aggregation may not be enough, and that more advanced deidentifying techniques are needed. In the Netflix case, individuals in an anonymized publicly available database of customer movie recommendations from Netflix were reidentified by linking their ratings with ratings in the Internet movie rating Web site IMDB.46 In the AOL case,47 a reporter reidentified an AOL user in released “deidentified” search queries, and revealed that a combination of several queries was enough to narrow the searcher’s identity to 1 particular person.48 Privacy breaches are often reported in the popular press,49 and represent a strong disincentive for sharing data.
To avoid privacy pitfalls and to mitigate risk, numerous articles have been published to setup a foundation of privacy-preserving data publishing for general and specific applications.50,51,80 One line of approach, including k-anonymity, as introduced earlier, manipulates the data to merge unique individuals, sanitizing tables through table “anonymization”33,81,82 (ie, generalization or suppression) before publication. Another approach to this kind of data sharing is producing synthetic data, which are supposed to capture the features of the original data. In this context, this would involve generating fictitious patients, who “look like” the real patients. Several methods have been proposed that do not explicitly quantify privacy,83 adopt novel risk measures,84 or use a blend of anonymized and synthetic data.85 Others create compact synopses, including wavelets,59 trees,86 contingency tables,87,88 and compressed bases,89 and sample synthetic data from the synopsis. In the literature on differential privacy, synthetic data generation has attracted significant interest for a theoretical standpoint90 (see also follow-up work91), but there are limited studies to evaluate the usefulness of differentially private synthetic data in real-world applications.92,93
Although we identified a few privacy technologies that can facilitate CER research, to realize the full potential of CER studies in a privacy-sensitive way, more work has to be done to bridge the gap between CER researchers, statisticians, informaticians, and computer scientists. In particular, these communities can work together to develop more precise formulations of CER data-sharing problems, benchmarks for privacy and utility, and realistic expectations of how much protection must come from technology (algorithms) versus policy (use agreements).
CER researchers can contribute by more concretely specifying their data-sharing needs. For example, for a large multisite study, what information really needs to be shared? Perhaps a preliminary assessment would show that some portions of the raw records are not needed. By developing canonical data sharing and study examples, designers can develop algorithms that are tuned to those settings.
Statisticians who work on CER studies are best positioned to specify the kinds of inference procedures they need to run on the data. This in turn will inform algorithm design to help minimize the distortion in those inferences while still preserving privacy. Not enough work has been done to develop meaningful utility metrics. There is a rich literature on enhancing data utility during anonymization54,94–97; however, the metrics vary widely.24,97,98 It is important to develop standards for utility and data quality that are relevant for CER applications. These in turn can dictate the kinds of policy protections and algorithmic parameters to use in anonymization. By integrating the statistical task to be performed with the data sharing structure for the CER study, researchers can develop a concrete and well-specified problem for algorithm designers.
The last piece is to develop a set of comprehensive benchmarks on standardized data that other research communities such as the machine learning and computer vision communities use to compare and validate novel models. Such benchmarks can be used to provide head-to-head comparisons of existing privacy-preserving technologies. This requires the work of all parties to find concrete examples and corresponding data for each of these canonical data-sharing examples. This research reproducibility will steer the development of algorithms by making it clear which ones are successful.
The field of CER evolves rapidly. New emerging applications may involve new data types and there might be no privacy standards to protect them. Such a gap between policy and technology calls for substantial future development of new standards of health care data privacy protection for genomic data,99–103 set-valued data,104 time series data,105 text data,106,107 and image data,108 which have not been adequately studied in the privacy perspective.
As we described in the previous section, many of the new anonymization and privacy-preserving data publishing techniques can be applied to scenarios of interest in CER. Some of these approaches are still under active development, and choosing privacy metrics and algorithms will depend not only on the data-sharing structure but also on the specific data to be shared and policy considerations. Data-sharing agreements can mitigate the loss of utility in anonymized data at the expense of more policy oversight. Entities such as an Institutional Review Boards exist in many organizations and can provide guidelines on data use to prevent researchers from inappropriately using the shared data to reidentify individuals. For example, in institution-to-institution data-sharing arrangements, enforceable contracts can be signed between the institutions to guarantee oversight of the shared data and to describe appropriate uses for the data. For hosted-access models, users who wish to access the data could sign use agreements that restrict how they can disclose the information; such models are used routinely by government agencies in data enclaves such as the National Opinion Research Center.109 The greatest danger comes from public dissemination of data, where there can be no reasonable restrictions placed on the public’s use of the data. In such a setting, privacy protections must be correspondingly stronger and more comprehensive.
Ultimately, the choice of privacy level will be dictated by a combination of policy considerations applied to these tradeoffs. Improved data governance policies and data-sharing agreements could help mitigate the impact that privacy-preserving operations have on utility by providing a technological and legal framework for preventing misuse of patient data. Privacy-preserving data manipulation is an important part of a larger data-governance ecosystem that encompasses informed consent, data use agreements, and secure data repositories.
Although there is a substantial and growing literature on privacy-preserving techniques in CS, statistics, social science, and medicine, many of these works are not directly applicable to the CER context. We surveyed state-of-the-art literature to find relevant papers, sort them, and make recommendations based on 3 major axes of CER applications (ie, institution-to-institution, institution hosted, and public release). Despite encouraging findings, we also identified a serious gap between theory and practice. To close this gap, CER researchers should specify statistical objectives from data sharing and privacy researchers should develop methods adapted to these objectives. New methods will be needed to handle more complex forms of data that arise in health care.
Obtaining real clinical benchmark data and initiating competitions between privacy technologies using that data, researchers can help build a healthy ecosystem between the CER and privacy communities. Such an exchange can encourage the sharing of ideas and development of real testable standards and benchmarks. Addressing these issues and overcoming challenges will catalyze the CER studies of the future.
3. Benitez K, Loukides G, Malin BA.Beyond safe harbor: automatic discovery of health information de-identification policy alternatives. Paper presented at: The 1st ACM International Health Informatics Symposium, 2010.
4. Baumer D, Earp JB, Payton FC.Privacy of medical records: IT implications of HIPAA.ACM Computers and Society (SIGCAS).2000;30:40–47.
6. .National Consumer Health Privacy Survey.2005.Oakland, CA:California HealthCare Foundation;1–5.
7. McGraw D.Building public trust in uses of Health Insurance Portability and Accountability Act de-identified data.J Am Med Inform Assoc.2012;20:29–34.
8. Tacconelli E.Systematic reviews: CRD’s guidance for undertaking reviews in health care.Lancet Infect Dis.2010;10:1–232.
9. Murphy SN, Gainer V, Mendis M, et al..Strategies for maintaining patient privacy in i2b2.J Am Med Inform Assoc.2011;18suppl 1103–108.
10. Vinterbo SA, Sarwate AD, Boxwala AA.Protecting count queries in study design.J Am Med Inform Assoc.2012;19:750–757.
11. Fefferman NH, O’Neil EA, Naumova EN.Confidentiality and confidence: is data aggregation a means to achieve both?J Public Health Policy.2005;26:430–449.
12. Matthews GJ, Harel O.Data confidentiality: a review of methods for statistical disclosure limitation and methods for assessing privacy.Stat Surv.2011;5:1–29.
13. Fayyoumi E, Oommen BJ.A survey on statistical disclosure control and micro-aggregation techniques for secure statistical databases.Softw Pract Exper.2010;40:1161–1188.
14. Ohno-Machado L, Silveira P, Vinterbo S.Protecting patient privacy by quantifiable control of disclosures in disseminated databases.Int J Med Inf.2004;73:599–606.
15. Ohrn A, Ohno-Machado L.Using Boolean reasoning to anonymize databases.Artif Intell Med.1999;15:235–254.
16. Jiang X, Cheng S, Ohno-Machado L.Quantifying Record-Wise Data Privacy and Data Representativeness. Proceedings of the 2011 Workshop on Data Mining for Medicine and Healthcare.2011.San Diego, CA:ACM;64–67.
17. Fung BCM, Wang K, Chen R, et al..Privacy-preserving data publishing.ACM Comput Surv.2010;42:1–53.
18. Dwork C.Differential privacy: a survey of results.Theory Appl Models Comput.2008;4978:1–19.
19. Bertino E, Lin D, Jiang W.A survey of quantification of privacy preserving data mining algorithms.Privacy Preserving Data Mining.2008;34:183–205.
20. Zhao Y, Du M, Le J, et al..A survey on privacy preserving approaches in data publishing. Paper presented at: 2009 First International Workshop on Database Technology and Applications, 2009.
21. Sweeney L.k
-anonymity: a model for protecting privacy.Int J Uncertainty Fuzziness Knowl Based Syst.2002;10:557–570.
22. Sweeney L.Datafly: a system for providing anonymity in medical data. Paper presented at: Eleventh International Conference on Database Security XI: Status and Prospects, 1998, Chalkidiki, Greece.
23. Machanavajjhala A, Kifer D, Gehrke J, et al..l
-diversity: privacy beyond k-anonymity.ACM Trans Knowl Discov Data (TKDD).2007;1:1–47.
24. Li N, Li T, Venkatasubramanian S.t
-closeness: privacy beyond k
-anonymity and l
-diversity. Paper presented at: The 23rd International Conference on Data Engineering (ICDE), 2007.
25. Xiao X, Tao Y.M-invariance: towards privacy preserving re-publication of dynamic datasets. Paper presented at: Proceedings of the 2007 ACM SIGMOD international conference on Management of data, 2007.
26. Cormode G, Srivastava D, Li N, et al..Minimizing minimality and maximizing utility: analyzing method-based attacks on anonymized data.Proc VLDB Endow.2010;3:1045–1056.
27. Ganta SR, Kasiviswanathan SP, Smith A.Composition attacks and auxiliary information in data privacy. Paper presented at: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining—KDD’082008; New York, NY.
28. Domingo-Ferrer J, Torra V.A Critique of k-Anonymity and Some of Its Enhancements: IEEE, 2008.
29. Wong RC-W, Fu AW-C, Wang K, et al..Anonymization-based attacks in privacy-preserving data publishing.ACM Trans Database Syst.2009;34:1–46.
30. Wong RC-W, Fu AW-C, Wang K, et al..Can the utility of anonymized data be used for privacy breaches?ACM Transact Knowl Discov Data (TKDD).2011;5:1–24.
31. Sacharidis D, Mouratidis K, Papadias D.k-anonymity in the presence of external databases.IEEE Trans Knowl Data Eng.2010;22:392–403.
32. Cormode G.Personal privacy vs. population privacy: learning to attack anonymization. Paper presented at: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011, New York, NY.
33. Nergiz ME, Atzori M, Clifton C.Hiding the Presence of Individuals From Shared Databases. International Conference on Management of Data (SIGMOD).2007.New York, NY:ACM Press;665–676.
34. Nergiz ME, Clifton C.δ-presence without complete world knowledge.IEEE Trans Knowl Data Eng.2010;22:868–883.
35. Muralidhar K, Sarathy R.Does differential privacy protect terry gross’ privacy? Paper presented at: Proceedings of the 2010 international conference on Privacy in statistical databases, 2010, Corfu, Greece.
36. Domingo-Ferrer J, Mateo-Sanz JM.Practical data-oriented microaggregation for statistical disclosure control.IEEE Trans Knowl Data Eng.2002;14:189–201.
37. Domingo-Ferrer J, Torra V.Ordinal, continuous and heterogeneous k-anonymity through microaggregation.Data Mining Knowl Discov.2005;11:195–212.
38. Bayardo RJ, Agrawal R.Data privacy through optimal k-anonymization. Paper presented at: The 21st International Conference on Data Engineering (ICDE), 2005.
39. Aggarwal G, Feder T, Kenthapadi K, et al..Anonymizing tables.Database Theory ICDT 2005.2005;3363:246–258.
40. Chi-Wing R, Li J, AW-C Fu, et al..(α, k)-anonymity. Paper presented at: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining—KDD’062006, New York, NY.
41. Ghinita G, Karras P, Kalnis P, et al..A framework for efficient data anonymization under privacy and accuracy constraints.ACM Trans Database Syst.2009;34:1–47.
42. LeFevre K, DeWitt DJ, Ramakrishnan R.Mondrian Multidimensional K-Anonymity. Paper presented at: 22nd International Conference on Data Engineering (ICDE’06), 2006.
43. Friedman A, Wolff R, Schuster A.Providing k-anonymity in data mining.VLDB J.2007;17:789–804.
44. LeFevre K, DeWitt DJ, Ramakrishnan R.Workload-aware anonymization techniques for large-scale datasets.ACM Trans Database Syst.2008;33:1–47.
45. Zhang N, Zhao W.Privacy-preserving OLAP: an information-theoretic approach.IEEE Trans knowl Data Eng.2011;23:122–138.
46. Narayanan A, Shmatikov V.Robust De-anonymization of Large Sparse Datasets. Paper presented at: 2008 IEEE Symposium on Security and Privacy (sp 2008), 2008.
47. Hansell S. Removes search data on vast group of web users. New York Times
48. Lasko TA, Vinterbo SA.Spectral anonymization of data.IEEE Trans Knowl Data Eng.2010;22:437–446.
49. El Emam K, Dankar FK.Protecting privacy using k-anonymity.J Am Med Inform Assoc.2008;15:627.
50. Fung BCM, Wang K, Chen R, et al..Privacy-preserving data publishing: a survey of recent developments.ACM Comput Surv (CSUR).2009;42:1–53.
51. Fung BCM, Wang K, Wang L, et al..Privacy-preserving data publishing for cluster analysis.Data Knowl Eng.2009;68:552–575.
52. El Emam K, Dankar FK, Issa R, et al..A globally optimal k-anonymity method for the de-identification of health data.J Am Med Inform Assoc.2009;16:670–682.
53. Mohammed N, Fung BCM, Hung PCK, et al..Centralized and distributed anonymization for high-dimensional healthcare data.ACM Trans Knowl Discov Data.2010;4:18:11–18:33.
54. Kifer D, Gehrke J.Injecting utility into anonymized datasets. Paper presented at: Proceedings of the 2006 ACM SIGMOD international conference on Management of data—SIGMOD’062006, New York, NY.
55. Ohno-Machado L, Vinterbo S, Dreiseitl S.Effects of data anonymization by cell suppression on descriptive statistics and predictive modeling performance.J Am Med Inform Assoc.2002;9:115S–119S.
56. Gouweleeuw J, Kooiman P, Willenborg L, et al..Post randomisation for statistical disclosure control: theory and implementation.J Offical Stat.1998;14:463–478.
57. Vinterbo SA, Sarwate AD, Boxwala A.Protecting count queries in cohort identification. Paper presented at: AMIA Summit on Clinical Research Informatics (CRI’11), 2011, San Francisco.
58. Barak B, Chaudhuri K, Dwork C, et al..Privacy, accuracy, and consistency too: a holistic solution to contingency table release. Paper presented at: Principles of Database Systems (PODS), 2007, Beijing.
59. Xiao X, Wang G, Gehrke J.Differential privacy via wavelet transforms.IEEE Trans Knowl Data Eng.2011;23:1200–1214.
60. Mishra DK.Tutorial: Secure Multiparty Computation for Cloud Computing Paradigm. 2010 Second International Conference on Computational Intelligence, Modelling and Simulation: xxiv-xxv, 2010, Bali, Indonesia.
61. Lindell Y, Pinkas B.Secure multiparty computation for privacy-preserving data mining.J Privacy Confidentiality.2009;1:59–98.
62. Clifton C, Kantarcioglu M, Vaidya J.Privacy-Preserving Data Mining.2006;Vol 180New York, NY:Springer-Verlag.
63. Vaidya J, Yu H, Jiang X, et al..Privacy-preserving SVM classification.Knowl Inf Syst.2008;14:161–178.
64. Wu Y, Jiang X, Kim J, et al..Grid LOgistic REgression (GLORE): building shared models without sharing data.J Am Med Inform Assoc.2012;19:758–764.
65. Inan A, Kaya SV, Saygin Y, et al..Privacy preserving clustering on horizontally partitioned data.Data Knowl Eng.2007;63:646–666.
66. Vaidya J, Clifton CW.Privacy preserving association rule mining in vertically partitioned data. Paper presented at: Proceedings of the Eighth ACMSIGKDD International Conference on Knowledge Discovery and Data Mining, 2002, Edmonton, Canada.
67. El Emam K, Hu J, Mercer J, et al..A secure protocol for protecting the identity of providers when disclosing data for disease surveillance.J Am Med Inform Assoc.2011;18:212.
68. Xu Z, Yi X.Classification of privacy-preserving distributed data mining protocols. Paper presented at: The Sixth International Conference on Digital Information Management, 2011.
69. Zhan J.Privacy-preserving collaborative data mining.IEEE Comput Intell Mag.2008;3:31–41.
70. Zhang Q, Koudas N, Srivastava D, et al..Aggregate query answering on anonymized tables. Paper presented at: The 23rd International Conference on Data Engineering (ICDE), 2007.
71. Nergiz ME, Tamersoy A, Saygin Y.Instant anonymization.ACM Trans Database Syst.2011;36:1–33.
72. Dinur I, Nissim K.Revealing information while preserving privacy. Paper presented at: Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, 2003, New York, NY.
73. Blum A, Dwork C, McSherry F, et al..Practical privacy: the SuLQ framework. Paper presented at: Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, 2005, New York, NY.
74. Dwork C, McSherry F, Nissim K, et al..Calibrating noise to sensitivity in private data analysis. Paper presented at: Theory of Cryptography Conference (TCC), 2006, New York, NY.
75. Dwork C.Differential privacy. Automata, languages and programming. Springer: Berlin, Heidelberg: 2006:1–12.
76. Roth A, Roughgarden T.Proceedings of the 42nd ACM symposium on Theory of computing—STOC’102010.
77. McSherry F.Privacy integrated queries: an extensible platform for privacy-preserving data analysis.Commun ACM.2010;53:89.
78. Friedman A, Schuster A.Data mining with differential privacy. Paper presented at: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD’10), 2010, New York, NY.
79. Roy I, Setty STV, Kilzer A, et al..Airavat: Security and privacy for MapReduce. Paper presented at: Proceedings of the 7th USENIX conference on Networked systems design and implementation, 2010.
80. Chaytor R, Wang K, Brantingham P.Fine-Grain Perturbation for Privacy Preserving Data Publishing. Paper presented at: 2009 Ninth IEEE International Conference on Data Mining, 2009.
81. Wong R, Li J, Fu A, et al..(alpha, k)-anonymous data publishing.J Intell Inf Syst.2009;33:209–234.
82. Jin X, Zhang M, Zhang N, et al..Versatile publishing for privacy preservation. Paper presented at: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), 2010, New York, NY.
83. Arasu A, Kaushik R, Li J.Data generation using declarative constraints. Paper presented at: Proceedings of the 2011 international conference on Management of data (SIGMOD 2011), 2011, New York, NY.
84. Cano I, Torra V.Generation of synthetic data by means of fuzzy c-Regression. Paper presented at: 2009 IEEE International Conference on Fuzzy Systems, 2009.
85. Domingo-Ferrer J, González-Nicolás Ú.Hybrid microdata using microaggregation.Inf Sci.2010;180:2834–2844.
86. Hay M, Rastogi V, Miklau G, et al..Boosting the accuracy of differentially private histograms through consistency.VLDB Endow.2010;3:1021–1032.
87. Barak B, Chaudhuri K, Dwork C, et al..Privacy, accuracy, and consistency too: a holistic solution to contingency table release. Paper presented at: Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, 2007, New York, NY.
88. Kasiviswanathan SP, Rudelson M, Smith A, et al..The price of privately releasing contingency tables and the spectra of random matrices with correlated rows. Paper presented at: Proceedings of the 42nd ACM symposium on Theory of computing (STOC'10), 2010, New York, NY.
89. Li Y, Zhang Z, Winslett M, et al..Compressive Mechanism: Utilizing sparse representation in differential privacy. Paper presented at: The Workshop on Privacy in the Electronic Society (WPES), 2011, Chicago, IL.
90. Blum A, Ligett K, Roth A.A learning theory approach to non-interactive database privacy. Paper presented at: Proceedings of the 40th annual ACM symposium on Theory of computing, 2008, New York, NY.
91. Wasserman L, Zhou S.A statistical framework for differential privacy.J Am Stat Assoc.2010;105:375–389.
92. Mohammed N, Chen R, Fung BCM, et al..Differentially private data release for data mining. Paper presented at: International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2011, San Diego, CA.
93. Machanavajjhala A, Kifer D, Abowd J, et al..Privacy: Theory meets Practice on the Map. 2008 IEEE 24th International Conference on Data Engineering (ICDE). IEEE. 2008; Washington DC, USA:277–286.
94. Tao Y, Chen H, Xiao X, et al..ANGEL: enhancing the utility of generalization for privacy preserving publication.IEEE Trans Knowl Data Eng.2009;21:1073–1087.
95. Rastogi V, Suciu D, Hong S.The boundary between privacy and utility in data publishing. Paper presented at: The 33rd International Conference on Very Large Data Bases (VLDB), 2007.
96. Goldberger J, Tassa T.Efficient anonymizations with enhanced utility. Paper presented at: 2009 IEEE International Conference on Data Mining Workshops, 2009.
97. Domingo-Ferrer J, Rebollo-Monedero D.Measuring risk and utility of anonymized data using information theory. Paper presented at: Proceedings of the 2009 EDBT/ICDT Workshops on—EDBT/ICDT’092009, New York, NY.
98. Rebollo-Monedero D, Forné J, Soriano M.An algorithm for k-anonymous microaggregation and clustering inspired by the design of distortion-optimized quantizers.Data Knowl Eng.2011;70:892–921.
99. Malin BA, Sweeney LA.Determining the identifiability of DNA database entries. Proceedings AMIA Symposium. 2000; Los Angeles, CA:537–541.
100. Malin BA.An evaluation of the current state of genomic data privacy protection technology and a roadmap for the future.J Am Med Inform Assoc.2005;12:28–34.
101. Malin BA, Sweeney LA.How (not) to protect genomic data privacy in a distributed network: using trail re-identification to evaluate and design anonymity protection systems.J Biomed Inform.2004;37:179–192.
102. Malin BA, Loukides G, Benitez K, et al..Identifiability in biobanks: models, measures, and mitigation strategies.Hum Genet.2011;130:383–392.
103. Malin BA, Sweeney LA.Inferring genotype from clinical phenotype through a knowledge based algorithm. Pac Symp Biocomput. 2002; Singapore:41–52.
104. Chen R, Mohammed N, Fung BCM, et al..Publishing Set-Valued Data via Differential Privacy. The 37th International Conference on Very Large Data Bases (in press), 2011.
105. Wang K, Xu Y, RC-W Wong, et al..Anonymizing temporal data. Paper presented at: International Conference on Data Mining, 2010.
106. Szarvas G, Farkas R, Busa-Fekete R.State-of-the-art anonymization of medical records using an iterative machine learning framework.J Am Med Inform Assoc.2007;14:574–580.
107. Gardner J, Xiong L, Li K, et al..HIDE: heterogeneous information DE-identification. Paper presented at: Proceedings of the 12th International Conference on Extending Database Technology Advances in Database Technology—EDBT’092009, New York, NY.
108. Li L, Wang JZ.DDIT—A tool for DICOM brain images de-Identification. The 5th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). Wuhan, China. IEEE.20111–4.