Personal data is collected and stored more than ever by the governments and companies in the digital age. Even though the data is only released after anonymization, deanonymization is possible by joining different datasets. This puts the privacy of individuals in jeopardy. Furthermore, data leaks can unveil personal identifiers of individuals when security is breached. Processing the leaked dataset can provide even more information than what is visible to naked eye. In this work, we report the results of our analyses on the recent "Turkish citizen database leak", which revealed the national identifier numbers of close to fifty million voters, along with personal information such as date of birth, birth place, and full address. We show that with automated processing of the data, one can uniquely identify (i) mother's maiden name of individuals and (ii) landline numbers, for a significant portion of people. This is a serious privacy and security threat because (i) identity theft risk is now higher, and (ii) scammers are able to access more information about individuals. The only and utmost goal of this work is to point out to the security risks and suggest stricter measures to related companies and agencies to protect the security and privacy of individuals.
Hackers or attackers have many techniques to obtain unauthorized information from the leaked or publicly shared (anonymized) data. The most well-known technique that can be used to learn more about individuals is profile matching (or deanonymization). Studies show that in today's digital world, anonymization is not an effective way of protecting sensitive data. For example, Latanya Sweeney showed that it is possible to de-anonymize individuals by using publicly available anonymized health records and other auxiliary information that can be publicly accessed on the Internet @cite_4 . It has been shown that anonymization is also an ineffective technique for sharing genomic data @cite_16 @cite_6 . For instance, genomic variants on the Y chromosome are correlated with the last name (for males). This last name can be inferred using public genealogy databases. With further effort (e.g., using voter registration forms) the complete identity of the individual can also be revealed @cite_6 . Also, unique features in patient-location visit patterns in a distributed health care environment can be used to link the genomic data to the identity of the individuals in publicly available records @cite_15 .
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Publishing data for analysis from a table containing personal records, while maintaining individual privacy, is a problem of increasing importance today. The traditional approach of de-identifying records is to remove identifying fields such as social security number, name etc. However, recent research has shown that a large fraction of the US population can be identified using non-key attributes (called quasi-identifiers) such as date of birth, gender, and zip code [15]. Sweeney [16] proposed the k-anonymity model for privacy where non-key attributes that leak information are suppressed or generalized so that, for every record in the modified table, there are at least k−1 other records having exactly the same values for quasi-identifiers. We propose a new method for anonymizing data records, where quasi-identifiers of data records are first clustered and then cluster centers are published. To ensure privacy of the data records, we impose the constraint that each cluster must contain no fewer than a pre-specified number of data records. This technique is more general since we have a much larger choice for cluster centers than k-Anonymity. In many cases, it lets us release a lot more information without compromising privacy. We also provide constant-factor approximation algorithms to come up with such a clustering. This is the first set of algorithms for the anonymization problem where the performance is independent of the anonymity parameter k. We further observe that a few outlier points can significantly increase the cost of anonymization. Hence, we extend our algorithms to allow an e fraction of points to remain unclustered, i.e., deleted from the anonymized publication. Thus, by not releasing a small fraction of the database records, we can ensure that the data published for analysis has less distortion and hence is more useful. Our approximation algorithms for new clustering objectives are of independent interest and could be applicable in other clustering scenarios as well. In today's data-rich networked world, people express many aspects of their lives online. It is common to segregate different aspects in different places: you might write opinionated rants about movies in your blog under a pseudonym while participating in a forum or web site for scholarly discussion of medical ethics under your real name. However, it may be possible to link these separate identities, because the movies, journal articles, or authors you mention are from a sparse relation space whose properties (e.g., many items related to by only a few users) allow re-identification. This re-identification violates people's intentions to separate aspects of their life and can have negative consequences; it also may allow other privacy violations, such as obtaining a stronger identifier like name and address.This paper examines this general problem in a specific setting: re-identification of users from a public web movie forum in a private movie ratings dataset. We present three major results. First, we develop algorithms that can re-identify a large proportion of public users in a sparse relation space. Second, we evaluate whether private dataset owners can protect user privacy by hiding data; we show that this requires extensive and undesirable changes to the dataset, making it impractical. Third, we evaluate two methods for users in a public forum to protect their own privacy, suppression and misdirection. Suppression doesn't work here either. However, we show that a simple misdirection strategy works well: mention a few popular items that you haven't rated. The increasing integration of patient-specific genomic data into clinical practice and research raises serious privacy concerns. Various systems have been proposed that protect privacy by removing or encrypting explicitly identifying information, such as name or social security number, into pseudonyms. Though these systems claim to protect identity from being disclosed, they lack formal proofs. In this paper, we study the erosion of privacy when genomic data, either pseudonymous or data believed to be anonymous, are released into a distributed healthcare environment. Several algorithms are introduced, collectively called RE-Identification of Data In Trails (REIDIT), which link genomic data to named individuals in publicly available records by leveraging unique features in patient-location visit patterns. Algorithmic proofs of re-identification are developed and we demonstrate, with experiments on real-world data, that susceptibility to re-identification is neither trivial nor the result of bizarre isolated occurrences. We propose that such techniques can be applied as system tests of privacy protection capabilities. Genomic datasets are often associated with sensitive phenotypes. Therefore, the leak of membership information is a major privacy risk. Genomic beacons aim to provide a secure, easy to implement, and standardized interface for data sharing by only allowing yes no queries on the presence of specific alleles in the dataset. Previously deemed secure against re-identification attacks, beacons were shown to be vulnerable despite their stringent policy. Recent studies have demonstrated that it is possible to determine whether the victim is in the dataset, by repeatedly querying the beacon for his her single nucleotide polymorphisms (SNPs). In this work, we propose a novel re-identification attack and show that the privacy risk is more serious than previously thought. Using the proposed attack, even if the victim systematically hides informative SNPs (i.e., SNPs with very low minor allele frequency -MAF-), it is possible to infer the alleles at positions of interest as well as the beacon query results with very high confidence. Our method is based on the fact that alleles at different loci are not necessarily independent. We use the linkage disequilibrium and a high-order Markov chain-based algorithm for the inference. We show that in a simulated beacon with 65 individuals from the CEU population, we can infer membership of individuals with 95 confidence with only 5 queries, even when SNPs with MAF less than 0.05 are hidden. This means, we need less than 0.5 of the number of queries that existing works require, to determine beacon membership under the same conditions. We further show that countermeasures such as hiding certain parts of the genome or setting a query budget for the user would fail to protect the privacy of the participants under our adversary model.