INTRODUCTION
The explosive growth of internet has increased the dependence of both organizations
and individuals on sharing information universally. This has led to an everincreasing
demand to protect information from unintended use and to guarantee the privacy
of involved parties as highlighted by a few incidents. In October, 2006, Netflix,
the world’s largest online DVD rental service, announced the $1million
prize for improving their recommendation service and publicly released 100,
480, 507 movie ratings by 480, 189 subscribers for contestants to use. Narayanan
and Shmatikov (2006) demonstrated that 96% of Netflix subscribers can be
uniquely identified with knowledge of no more than 8 movie ratings and dates.
Thus, the second Netflix Prize competition had to be called off at the last
minute due to the privacy issues.
In August of 2006, American Online provided an extremely large query log representing
20 million queries issued by 650 K users to help researchers in the information
retrieval community. Barbaro and Zeller (2006) demonstrated
the ease to determine the real identity of an anonymous AOL searcher, Thelma
Arnold, a 62yearold woman who lives in Lilburn, Georgia, by examining query
terms even ignoring the existence of social security numbers, driver license
numbers and credit card numbers.
Privacy preserving data publishing, abbreviated as PPDP, emerged to address
the privacy issues as illustrated by the preceding incidents.

Fig. 1: 
Process model of PPDP 
As discussed in Liu (2010) and summarized by the process
model in Fig. 1, PPDP addresses such a problem: A trusted
data publisher, e.g., Alice in a clinic, collects personal data of individuals,
e.g., patients such as Ahmed, in the raw form, anonymizes the collected data
and releases the anonymized data to a data recipient or the public, e.g., Bob
in a medical research centre. The data publisher, Alice, is responsible for
preventing the data recipient, Bob, from breaching the privacy of the individuals,
Ahmed, and for retaining usefulness of the anonymized data for data analysis,
e.g., for Bob to conduct medical research. The challenge is that the legitimate
data recipient, Bob, could be a privacy adversary which makes PPDP even harder
than information security.
Since the pioneering work by Samarati and Sweeney (1998),
research communities have proposed many approaches to PPDP. While the research
field is still rapidly developing, it is a good time to discuss the assumptions
and principles of PPDP and systematically evaluate different approaches to PPDP.
In particular, the state of the art research works on PPDP are summarized along
five dimensions, namely privacy protection principles, anonymization techniques,
data utility metrics, algorithms, challenges and future directions.
PRIVACY PRINCIPLES
Generally speaking, privacy is the claim of individuals to control when, how and to what extent information about them is communicated to others. A privacy protection principle enables users to specify the level of privacy protection against a certain type of privacy risk. In PPDP, kanonymity and ldiversity are well known principles.
kAnonymity: Samarati and Sweeney (1998) in
their pioneering work discussed an example of PPDP depicted in Fig.
1, where Alice in the clinic submits its patient data, after removing all
explicit identifying attributes, e.g., Name, to Bob in the medical research
centre. The released data as in Table 1a seems to be anonymous.
However, Bob happened to have a voter registration list as in Table
1b that is publically available and joined the two datasets on Age, Sex
and Zipcode to find out that Ahmed is the only person who contracted AIDS which
violates Ahmed’s privacy and is called linking attack.
To protect the identities of individuals whose records are in the data to be
released, Samarati and Sweeney (1998) proposed the kanonymity
principle. A dataset satisfies kanonymity if every individual’s record
is indistinguishable from at least k1 other records on quasiidentifier, i.e.,
attributes that can be used to link with external data, e.g., Age, Sex and Zipcode.
For example, data in Table 2a observes 2anonymity
by generalizing attributes Age and Zipcode, where records are partitioned into
two indistinguishable groups. The first indistinguishable group consists of
records 1, 3, 4 and 6 and the second is made of records 2 and 5.
Table 1: 
Patient data and voter registration list 

lDiversity: Machanavajjhala et al.
(2006) identified homogeneity attack on the sensitive attribute. For example,
attribute Disease is sensitive and the data are 2anonymous in Table
2a but the values of Disease in the second indistinguishable group (records
2 and 5) are homogeneous, i.e., all are Heart Attack which violates the privacy
of the two related persons.
To address the homogeneity attack, Machanavajjhala et
al. (2006) proposed the ldiversity principle which requires
that there are at least l wellrepresented values for a sensitive attribute
in each indistinguishable group of records. For example, data in Table
2b observes 2diversity, where records 1 and 2 comprise the first group
and others comprise the second group.
More privacy principles: Many privacy principles are intended to thwart specific privacy risks that were not addressed by kanonymity and ldiversity.
Privacy template was proposed by Wang et al. (2005)
which represents the privacy requirement by templates of the form: “A set
of identifying attributes”→ “the sensitive information to be
protected” associated with a maximum confidence threshold.
(X, Y)privacy was proposed by Wang and Fung (2006)
in considering the privacy attacks based on the linking between two disjoint
sets of attributes X and Y. It requires that each value on X is linked to at
least k distinct values on Y, namely (X, Y)anonymity and that the confidence
of inferring a value x on X to a value y on Y is less than a given threshold,
namely (X, Y)linkability. kAnonymity is the special case of (X,Y)anonymity
where X is the quasiidentifier and Y is a key that uniquely identifies records.
lDiversity has some similarity with (X,Y)linkability in that both bounds
the attacker’s inference confidence.
Personalized privacy was proposed by Xiao and Tao (2006b)
in addressing the major drawback of kanonymity and ldiversity, that
is, a universal approach that exerts the same amount of protection for all persons,
without considering their concrete needs.
Table 2: 
Anonymized patient data 

The idea is to let individuals to specify their levels of privacy that are expressed
by socalled guarding nodes. The guarding node of an individual is a node on the
taxonomy of the sensitive attribute till which he/she is comfortable with revealing
his/her information, assuming that the sensitive attribute is categorical and
have a taxonomy. The personalized privacy requirement for the individual is to
limit the breach probability of any leaf value under the guarding node within
a userdefined threshold.
tCloseness was proposed by Li et al. (2007)
which requires that the distance between the distribution of a sensitive attribute
in an indistinguishable group and the distribution of the attribute in the whole
data is no more than a threshold t. Notice that tcloseness has to be used with
kanonymity.
l^{+}Diversity was proposed by Liu and
Wang (2010b) which avoids a universal protection that could incur excessive
data distortion by setting a different privacy threshold for each sensitive
value according to its sensitivity. Such a notion provides better protection
for more sensitive values and incurs less data distortion.
(k,e)Anonymity was proposed by Zhang et al. (2007)
for protecting numerical sensitive attributes, while ldiversity considers
only categorical sensitive attribute. This principle requires that each indistinguishable
group of records holds at least k different sensitive (numerical) values and
the range of sensitive values in the group is no less than a threshold e.
(ε, m)Anonymity was proposed by Li et al.
(2008) to prevent the proximity attack on numerical sensitive attributes.
Such privacy attack occurs when an adversary concludes with a high confidence
that the sensitive value of a victim individual must fall in a short interval.
This principle demands that for every sensitive value x in an indistinguishable
group of records, at most 1/m of the records in the same group can have sensitive
values similar to x in the εneighborhood of x. This means sensitive values
should be well distributed, i.e., scattered in the whole range. (ε,m)Anonymity
remedies the drawback of (k,e)anonymity, that is, the ignorance of the distribution
of sensitive values within the group.
δPresence was proposed by Nergiz et al. (2007)
in considering a practical problem: The risk is simply from identifying that
an individual is (or is not) in an anonymized database. The idea is that anonymizing
such a database should mean that a recipient of the database should not be able
to identify any individual as being in that database with certainty greater
than δ.
εDifferential privacy was proposed by Dwork (2006)
in observing that there is no reasonable mechanism to protect certain type of
privacy (impossibility result) because of the external (auxiliary) information.
For example, given a statistical database of individuals’ heights and auxiliary
information which states that Alice is 2 inch taller than average height, no
matter Alice’s height is in the database or not there is no way to prevent
revealing Alice’s absolute height other than control of the access of the
database. Therefore, this principle requires that the participation of an object
in the database should have no significant difference on the query answers.
Given a database X with a domain D and a query (aggregate function) f with a
Range (f), for any two instances A and B of X that differ only by any row x,
the difference between the probability for a query answer S in Range (f) to
be derived from A and that from B should be less than ε, i.e., Pr [K(f
(A)) = S]/Pr [K (f (B)) = S]≤e^{ε}≈ 1 + ε, where
K is a randomization operator.
ANONYMIZATION TECHNIQUES
Anonymization refers to the PPDP approaches that aim to hide the identity and sensitive information of individuals by transforming data to observe a particular privacy principle. Anonymization techniques can be broadly categorized as generalization and suppression and perturbation.
Generalization and suppression techniques: Generalization involves replacing
specific values with a more general one. Suppression involves deleting values
or records. Value suppression can be deemed as generalizing a value to unknown
value *. LeFevre et al. (2005) categorizes generalization
models into two classes. The first class is hierarchybased models which use
fixed value generalization hierarchies and are more suitable for categorical
data. The second class is partitionbased models which require the domain of
an attribute to be a totally ordered set, define generalizations by partitioning
the set into disjoint ranges and are most suitable for numerical data. Generalization
models can also be categorized as follows.
Full domain generalization proposed by Samarati and Sweeney
(1998) requires that all generalized values of an attribute in the anonymized
data must be on the same level of the taxonomy tree of the attribute’s
domain.
Full subtree generalization proposed by Iyengar (2002)
requires that the child values sharing a common parent value are either all
or none generalized and each generalization is applied to all records. This
technique is more flexible than the full domain generalization.
Singledimension partitioning proposed by Iyengar (2002)
as well as Bayardo and Agrawal (2005) generalizes values
of an attribute that has a total order by partitioning the values of the attribute
into intervals independent of other attributes.
Multidimensional generalization proposed by LeFevre et
al. (2006) allows a generalization step to be locally applied to a multidimensional
region. e.g., data in Table 2 are full subtree generalization
of data in Table 1a, data in Table 3a are
multidimensional generalization of data in Table 1a. This
technique incurs a smaller information loss than the full subtree generalization
model but destroys domain exclusiveness in the generalized data.
Bucketization proposed by Xiao and Tao (2006a) publishes
the exact quasiidentifier values and sensitive values in two separate tables
QIT and ST and maintains the grouping information by a common attribute GID.
Such a method limits the associations among quasiidentifier values and sensitive
values. The drawback is that the adversary could easily confirm the participation
of a target individual which is a privacy breach in some cases (Nergiz
et al., 2007). Table 3b and c
is a bucketization of data in Table 1a.
Perturbation techniques: While generalization and suppression retain data semantics at the record level, perturbation techniques retain data semantics at the aggregate level. Perturbation techniques usually are based on randomizations.
Permutation was employed by Zhang et al. (2007)
to randomly permute the association between the quasiidentifier and the sensitive
attribute to enforce (k,e)anonymity.
Table 3: 
Multidimensional generalization and bucketization 

Additive random noise with uniform and Gaussian distribution was employed by
Agrawal and Srikant (2000) to perturb numerical data.
Additive random noise with a Laplace (0, σ) distribution was employed by
Dwork (2006) to enforce differential privacy with σ
being the sensitivity of the query answer to the presence/absence of any record.
Randomized response was employed by Du and Zhan (2003)
to disguise binary data. The idea is to negate all values in a record by a certain
probability which was extended by Huang and Du (2008)
to disguise categorical data.
DATA UTILITY METRICS
An important objective of PPDP is to retain as much data utility as possible while observing a privacy principle. A data utility metric can be used in guiding anonymization algorithms and in evaluating the usefulness of anonymized data. There are generalpurpose metrics as well as applicationspecific metrics. Generalpurpose metrics capture the notion to keep the data as specific as possible.
Generalization height was employed by Samarati and Sweeney
(1998) to measure the information loss. It is the height of an anonymized
data in the generalization lattice, i.e., the number of generalization steps
that were performed.
Loss metric (LM) was presented by Iyengar (2002) to
quantify the information loss by the ambiguity in generalizing a value which
depends on how many other value cannot be distinguished from it. When generalizing
values on a categorical attribute A_{Cat} in an indistinguishable group
G into a generalized value
, we have:
where,
is the information loss for each value on A_{cat} in G, #leaves (A_{cat})
is the number of leaves on the taxonomy tree associated with attribute A_{cat},
and leaves (
) is the number of leaves on the subtree rooted at
. When generalizing values on numerical attribute A_{Num} in an indistinguishable
group G into an interval
, we have:
where,
is the information loss for each value on A_{Num} in G and A_{Num}
is the width of the domain of A_{Num}. Xu et
al. (2006) proposed NCP and Ghinita et al.
(2007) proposed GCP, Liu and Wang (2010a) proposed
bLM, that are variants of LM.
Classification Metric (CM) was presented by Iyengar (2002)
to optimize the anonymized data for training a classifier, where each record
is associated with a class label. CM assigns no penalty to an unsuppressed record
that belongs to the majority class in its indistinguishable group. If a record
is suppressed or it belongs to minority classes, it is penalized by 1.
Discernibility Metric (DM) was proposed by Bayardo and
Agrawal (2005) which captures the desire to maintain discernibility between
records. DM assigns each retained record a penalty equal to the size of the
indistinguishable group the record belongs to, and each suppressed record a
penalty equal to the size of the whole dataset.
KLdivergence was proposed by Kifer and Gehrke (2006)
and employed by Ghinita et al. (2007) to compare
two distributions F_{1} and F_{2} which can be used in evaluating
the quality of an anonymized data. Let p_{i}^{(k)} be the probability
of x_{i} by F_{k}, KLdivergence is defined as follows
which is minimized when F_{1} = F_{2}:
Applicationspecific metrics capture the usefulness of the anonymized data in fulfilling a specific data mining task which is usually expressed as the accuracy of the data mining model built on the anonymized data and is only used in evaluating the quality of the anonymized data, rather than in guiding algorithms.
Classification error, defined as the gap between the prediction error of the
classifier built on the anonymized data and that on the raw data, was used by
Aggarwal and Yu (2004), Wang et
al. (2004), Wang et al. (2005), Fung
et al. (2005) and Wang and Fung (2006) to
measure data utility.
Recall and precision of frequent patterns were proposed by Liu
(2010) and Liu and Wang (2010a) to measure data
utility in terms of the percentage of frequent patterns retained in the anonymized
data and the precision of supports of the frequent patterns.
Average relative error in answering aggregate queries for a given query workload
was used by LeFevre et al. (2006) and Xiao
and Tao (2006a, b) to measure data utility which
compares the estimated result on the anonymized data and the actual result on
the original data.
ALGORITHMS
An algorithm transforms raw data by an anonymization technique to enforce a
privacy principle and to retain as much information as possible in terms of
a data utility metric.
Optimal search algorithms: Incognito proposed by LeFevre
et al. (2005) generates the set of all kanonymous fulldomain generalizations
with an optional record suppression threshold. Incognito makes use of the bottomup
aggregate computation technique and the a priori pruning. Incognito performed
up to an order of magnitude faster than counterparts, such as Binary Search
(Samarati, 2001) and MinGen (Sweeney
2002). Incognito was also adopted in finding full domain generalizations
that observe ldiversity by Machanavajjhala et
al. (2006) and tcloseness by Li et al. (2007).
kOptimize proposed by Bayardo and Agrawal (2005) is
the first practical algorithm that finds an optimal kanonymization with the
partitionbased single dimension recording with suppression which exploits the
monotonicity of kanonymity and is suitable for attributes whose domains have
a total order.
l^{+}Optimize by Liu and Wang (2010b)
and OTA by Liu (2011) are the first optimal full subtree
generalization algorithms that enforce ldiversity on relational data
and transactional data, respectively. l^{+}Optimize and OTA
improve efficiency by employing strong pruning based on information loss lower
bounding and a novel enumeration strategy, respectively.
Anatomy proposed by Xiao and Tao (2006a) finds a bucketization
in linear time which is optimal in the sense of minimizing the error of reconstructing
the probability density function of records. Ghinita et
al. (2007) improved Anatomy for a special case of one dimension quasiidentifier
(only one attribute) by taking into account the quasi identifier values when
grouping records.
Heuristic search algorithms: Mondrian proposed by LeFevre
et al. (2006) is a greedy algorithm of multidimension partitioning
for achieving kanonymity. Mondrian takes the whole data as a multidimensional
region and keeps on splitting any region R into two regions if R contains more
than 2k records until all existing regions have a size ≤2k1. Ghinita
et al. (2007) extended their optimal single dimension bucketization
algorithm to the multidimensional case by using spacemapping techniques such
as Hilbert space filling curve.
TDS proposed by Fung et al. (2005) is a well
known, greedily algorithm that finds a generalization solution by topdown,
greedy search guided by a goodness metric that measures the tradeoff between
the gain of information and loss of anonymity. Similar algorithms were proposed
by Wang et al. (2004) and Xiao
and Tao (2006b) and so on.
mHgHs proposed by Liu and Wang (2010c) is a greedy
algorithm that integrates full subtree generalization and suppression to enhance
data utility. A multiround, topdown greedy search approach was employed to
ensure the performance in anonymizing high dimensional and large datasets.
Approximation and other algorithms: O(klogk)approximation presented
by Meyerson and Williams (2004) finds an approximation
for the optimal problem of minimizing the diameter sum of hamming balls enclosing
indistinguishable groups. O(k)approximation presented by Aggarwal
et al. (2005) is another approximation for the optimal kanonymity
problem.
rGather proposed by Aggarwal et al. (2006)
is a constantfactor approximate clustering algorithm that is of general purpose
and independent of the size of clusters which can be applied in producing kanonymizations.
Liu and Wang (2010a) proposed a clustering algorithm
for kanonymity based on semantic similarities.
Genetic algorithm was employed by Iyengar (2002) for
finding a kanonymous full subtree generalization. Multiobjective optimization
was introduced by Huang and Du (2008) to find optimal
disguise matrices for the randomized response technique.
CHALLENGES AND FUTURE DIRECTIONS
PPDP is quite challenging as the legitimate data recipient is a privacy adversary which results in the hardness to reach the optimal balance between privacy and data utility. Therefore, several questions remain open: can an optimal solution with a flexible anonymization model gain utility significantly; can a customized privacy principle improve utility; is it practical to find an optimal solution efficiently for real world data? In addition to these challenges, we identify the following future research directions.
PPDP for new data sharing forms and new data types: New privacy problems are ever emerging with new forms of information sharing, e.g., cloud computing and its implications for the privacy of personal information and for the confidentiality of business information, privacy issues in locationbased services, privacy protection in social networks and privacy issues in sharing web search data.
PPDP integrated with information security technology: The everlasting
challenge with privacy preserving data publishing is that privacy and utility
are two contradicting goals. In many cases, it is hard to find a solution that
provides sufficient privacy protection and retains enough data utility. Integrating
privacy protection techniques with information security techniques could be
a promising approach.
PPDP with social and legal issues: Privacy protection is a complicated social and psychological issue that cannot be fully addressed by technical solutions and need to be studied from multiple perspectives. Interdisciplinary collaborative research is critical for providing real world privacy protection scenarios.
ACKNOWLEDGMENT
This study was supported in part by Zhejiang Natural Science Foundation (No.Y105700) and the Science and Technology Development Plan of Zhejiang Province (No. 2006C221034).