The document summarizes a literature review on protecting DNA sequence anonymity through generalization lattices. It discusses how DNA data poses privacy risks due to unique identification. The methodology applies k-anonymity and generalization to DNA data by grouping similar sequences to prevent re-identification. It proposes using DNALA to ensure each sequence is indistinguishable from at least k-1 others through generalization in a DNA domain hierarchy. The conclusion discusses challenges in data utility with increased generalization and potential alternatives like noise addition or synthetic data generation.

1. Bioinformatics Literature Review
Protecting DNA Sequence Anonymity with Generalization Lattices
(Malin, 2005)
Literature Review by Kato Mivule
COSC891 – Bioinformatics, Spring 2014
Bowie State University
Reference: Bradley. A. Malin, "Protecting genomic sequence anonymity with generalization lattices.", Methods of
information in medicine, Vol. 44, No. 5. (2005), pp. 687-692
Bowie State University Department of Computer Science
Image Source: U.S. National Library of Medicine

2. Outline
• The Problem
• Methodology
• Conclusion and Future work
Bowie State University Department of Computer Science
Bioinformatics Literature Review

3. The Problem
• Transactions in DNA data poses serious privacy concerns.
• DNA uniquely identifies an individual.
• DNA data is prone to re-identification and inference attacks.
Bowie State University Department of Computer Science
Bioinformatics Literature Review

4. The Problem:
Bowie State University Department of Computer Science
Bioinformatics Literature Review
Source: Forbes.com - April 25th 2013

5. Methodology
• Apply k-Anonymity
• Apply Generalization
• Apply the concept of generalization lattice to determine the distance between
two residues in a single nucleotide region, which offers the most similar
generalized concept for two residues – for example adenine and guanine are
both purines.
• DNALA – using k-anonymity by granting that the DNA sequence of one
individual will be similar to the DNA sequence of another individual.
Bowie State University Department of Computer Science
Bioinformatics Literature Review

6. Methodology
• k-anonymity
• K-anonymity uses both generalization and suppression to enforce
confidentiality.
• K-anonymity requires that for a data set with quasi-identifier attributes in a
database to be published, values in the quasi-identifier attributes must be
repeated at least k times to ensure privacy, with the value of k > 1.
• Because of the generalization and suppression features, k-anonymity is
applicable for DNA data privacy.
Bowie State University Department of Computer Science
Bioinformatics Literature Review

7. Methodology
Generalization
• Generalization is a data privacy method in which values in attributes that
could cause identify disclosure are made less informative by being replaced
with general values.
• An example is replacing age values of people born between 1970 and 1979 to
just 1970.
• Generalization follows the Domain Generalization Hierarchy (DGH), which
is different levels of generalization. For example we could use L1 =1970-09
and generalize to the month, L2 = 1970, generalize to the year, L3 = 197*
generalize to the decade.
Bowie State University Department of Computer Science
Bioinformatics Literature Review

8. Methodology
DNALA – DNA Lattice Anonymization
• Employs k-anonymity for data privacy
• The technique safeguards privacy by ensuring that the DNA sequence of one
individual will be precisely the same as the sequence of one other individual
in the published data set.
• When an institution publishes DNA sequence data using DNALA technique,
the uniqueness of every DNA sequence is assured to be inseparable from at
least k – 1 other identities.
Bowie State University Department of Computer Science
Bioinformatics Literature Review

9. Methodology
DNA Domain Generalization Hierarchy
Bowie State University Department of Computer Science
Bioinformatics Literature Review
Image source: Malin, (2005)

10. Methodology
DNA Domain Generalization Hierarchy
Bowie State University Department of Computer Science
Bioinformatics Literature Review
Image source: Malin, (2005)

11. Methodology
DNA Domain Generalization Hierarchy
Bowie State University Department of Computer Science
Bioinformatics Literature Review
Image source: Malin, (2005)

12. Methodology
DNALA Algorithm
Bowie State University Department of Computer Science
Bioinformatics Literature Review
Image source: Malin, (2005)

13. Conclusion and Future works
•DNA data privacy using k-anonymity is still promising.
•Data utility remains a challenge as more DNA sequence info gets generalized.
•How do other algorithms such as noise addition, and differential privacy apply?
•Could we generate synthetic and or obfuscated DNA data with similar traits as the
original?
Bowie State University Department of Computer Science
Bioinformatics Literature Review

14. References
1. Bradley. A. Malin, "Protecting genomic sequence anonymity with generalization lattices.", Methods
of information in medicine, Vol. 44, No. 5. (2005), pp. 687-692
2. K. Mivule and C. Turner, “Applying Data Privacy Techniques on Published Data in Uganda,” in
International Conference on e-Learning, e-Business, Enterprise Information Systems, and e-
Government (EEE), 2012, pp. 110–115.
3. Adam Tanner, Forbes.com "Harvard Professor Re-Identifies Anonymous Volunteers In DNA
Study", Forbes.com, 4/25/2013, Accessed: 02/10/2014, Available Online:
http://www.forbes.com/sites/adamtanner/2013/04/25/harvard-professor-re-identifies-anonymous-
volunteers-in-dna-study/
Bowie State University Department of Computer Science
Bioinformatics Literature Review