TF-SProD: Time Fading based Sensitive Pattern Hiding in Progressive Data
Shubham Bhatia and Durga Toshniwal
Department of Computer Science and Engineering
Indian Institute of Technology, Roorkee
Uttarakhand - India{shubham1695@gmail.com, durgatoshniwal@gmail.com}
Abstract
In the present era, there is a tremendous generation of transactional data due to e-commerce activities and traditional retail sales. Different organizations want to analyze the data generated bythem collaboratively to find some existing trends
and patterns in the global transactional data, which
may further lead to enhanced strategies for businesssales. But this may lead to the disclosure of somepatterns which are sensitive to an organization. In
order to avoid this, privacy preserving data mining
is used. The existing techniques for hiding sensitive patterns in transactional data consider the data
as static data. Static data means that the entire data
is present beforehand, and no changes are expectedin the data, once the processing starts. But most of
the data that is received today is dynamic in nature,
meaning that the old data may be considered obsolete while new data is being added. In this paper,a technique is proposed for hiding sensitive itemsets from a set of frequent itemsets in progressive
data. The proposed algorithms apply sensitive pattern hiding techniques to progressive data by using
a sliding window time fading model. A buffer isused to keep track of the top-k frequent itemsets until a given point in time. Both the frequency countof the frequent items and their time of occurence
are used to update the buffer. Experiments havebeen performed on synthetic datasets, and the results are very promising.

1

Introduction

The basic notion of information privacy is to have control
over handling and collecting an individual’s data. Collection of data from various sources may have many advantages,
but it may also lead to information leakage. To deal withinformation leakage, methods have been proposed, which
are known as Privacy Preserving Data Mining (PPDM) techniques. PPDM techniques work by modifying user’s original data. But this modification may lead to reduction of datautility. Thus PPDM methods are designed in a way so as tohide user’s data, while maximizing the data utility. Thesemethods deal with withdrawal of knowledge from a huge

database while preventing the revelation of sensitive information. The main problem with data mining techniques is thatpeople may infer some information which is sensitive to theuser, from non-sensitive information. This problem is widely
seen when one is trying to find out frequent itemsets from a
set of databases. During such an analysis, one may obtainsome frequent itemsets, which may have sensitive information about a particular user or organization. For example, if
an organization publishes its transactional information without hiding certain patterns, attackers may infer sensitive information from the non-sensitive patterns. To deal with theaforesaid problem, privacy preserving frequent itemset mining is done. The most common way of preserving privacy
is to reduce the support count of the sensitive items, to hidethem. This process of hiding the sensitive itemsets by reducing their support counts is known as sanitization of thedatabase. But these techniques come with their challenges,
and hence they must be designed keeping in mind that a) the
impact on the database due to the changes should be as minimal as possible, and b) a good trade-off should be guaranteed
between knowledge discovery and privacy.
Sensitive pattern hiding has been categorized into threedifferent approaches: Border-based approaches, exact approaches, and heuristic-based approach. Border-based approaches and exact approaches are not suitable for very large
datasets, because they are challenging to implement, andalso they are computationally expensive. As the data size
grows, the time taken by these approaches also increases.
Thus, heuristic based approaches are used mostly. These approaches decide the outcome based on local optima, hence
they are fast and simple to implement, as compared to theexact and border-based approaches.
Most of the above approaches deal with privacy preserving
frequent itemset mining on static data, meaning that the entire
data is present beforehand and no changes are expected in
the data once the data is processed. This paper proposes atechnique for privacy preserving frequent itemset mining on
progressive data. Progressive data is dynamic data, in thesense that it is updated regularly, and some data may fadeaway or may be considered obsolete while the new data is
being generated.
This paper has been organized into six sections. The firstsection gives a brief introduction about frequent itemset mining and the problems which occur when frequent itemset min-

ing is done collaboratively, for more than one organizations.
The introduction also discusses the different approaches ofsensitive itemset mining. The second section elaborates thestate of art methods and the work done in the area of sensitive itemset hiding. The third section explores the basic terminologies being used. The fourth section explains the proposed approach. The fifth section deals with the experiments
performed and the inference from the results of the experiments. Finally, the sixth section concludes the paper.

2

Related Work

Most of the work done in the field of privacy preserving data
mining has been done on static data. For example, Oliveira
and Zaiane in [Oliveira and Zaiane, 2002] proposed an approach, in which only in a few scans of the database, multiple itemsets are hidden. In the first scan of the database,an index file is created, which is used to find out sensitivetransactions corresponding to any sensitive itemset. In otherscans, data is sanitized so that minimal changes is done tonon-sensitive patterns. Three algorithms were introducedby them: MinFIA- Minimum Frequent Itemset Algorithm,
MaxFIA- Maximum Frequent Itemset Algorithm and IGAItemset Grouping Algorithm. In the MinFIA algorithm, sensitive transactions are identified, and then, according to theirdegree of conflict, they are sorted. Further, from each of thesetransactions, an item is removed, which is known as the victim item. Victim item is chosen as the item which has minimum support. MaxFIA works in the same fashion, but it selects the victim item as the one with maximum support. IGA
works by grouping common items of sensitive itemsets, andthe item with minimum support is chosen as victim.
According to the approach proposed by Cheng et al. in[Cheng et al., 2016], the very first step of the process is tostore the count of all non-sensitive patterns that a transaction
supports. Next, the count of each transaction is stored corresponding to each sensitive item, and these transactions are
sorted in accordance to their supports calculated in the firststep. Then victim items are removed from transactions. Victim item is the item in sensitive pattern which has maximum
support.
A confidence-based approach was proposed by Verykios etal. in [Verykios et al., 2004]. In this approach the hiding ofsensitive patterns is done by decreasing the confidence of an
association rule because lesser side effects are caused to thesanitized dataset. One limitation of this approach is that it
does not guarantee hiding of all sensitive itemsets.
The authors in [Jangra and Toshniwal, 2020] suggest a victim item deletion based sensitive pattern hiding technique, inwhich each particle of the population consists of n number ofsub- particles derived from pre-calculated victim items.
An approach for hiding sensitive patterns without any side
effects is suggested in [Surendra and Mohan, 2019]. The authors propose Recursive Pattern Sanitization (RPS) algorithm
which hides multiple sensitive itemsets irrespective of theirsupport count and size. This is done in single parse of theclosed patterns. The patterns retain the closeness property inthe sanitized model, and the model has inherent support forfinding frequent itemsets.

Few researches have been done in progressive data. For
frequent itemset mining in progressive data, various approaches have been proposed in [Mhatre and Toshniwal,
2010], [Mhatre et al., 2009] and [Thool and Voditel, 2013].
The broad classification of these approaches is counter basedalgorithms and sketch based algorithms. Counter based approaches generally maintain a counter variable, and increment the counter if the item is seen again. Space saving
and lossy counting algorithm are types of counter based approaches. In sketch based approaches, both arrival and departures of an item are kept in mind. Fernandez-Basso et. al. in[Fernandez-Basso et al., 2019] propose a method for finding
tendencies in streaming data using Big Data frequent itemset mining. They propose a frequent itemset mining method
using sliding windows capable of extracting frequently occurring items from continuous data flows. Yamamoto et. al. in[Yamamoto et al., 2019] propose a hybrid approximation approach for scalable frequent itemset mining in streaming data.
They argue that any streaming data algorithm must possess
two important properties; (i) the real time property, whichmeans the property to process a large volume of data which
arrives continuously at high speed and detect the frequent
itemsets simultaneously, and (ii) memory efficiency, whichmeans the property to maintain the frequent itemsets for theentire database, using limited amount of memory. Yun and
Lee in [Yun and Lee, 2016] discuss an approach for Incremental mining of weighted maximal frequent itemsets fromdynamic databases. They discuss the issues with stream data
mining, i.e., the results can be very large scale. Storing theseresults may be difficult to analyze and all of them may notconvey some meaningful information. Thus, they propose algorithm to extract smaller number of itemsets. Most of theexisting research works on progressive data, particularly the
space saving algorithm has its own disadvantages. The spacesaving algorithm takes into account only the support count of
the frequent itemsets. As and when a new item is received, if
the buffer can accommodate it, it is inserted into the buffer,otherwise the element with the minimum support is deleted
from the buffer and the new element is inserted. The spacesaving algorithm deletes the element only on the basis of its
support count, but it does not take into account the time atwhich the element occurred. In this paper, timestamps arealso taken into account while deleting an item from the buffer.
Most of the existing research works for hiding sensitive
patterns have been developed for static data but not for progressive data. Static data remain constant with time, in thesense that entire data is available at once, and no new items
are added / deleted. On the contrary, progressive data is more
complex to deal with. Progressive data is a stream of data,with continuous inflow of items. The new incoming data canincrease the support count of some items which were not frequent previously, and also remove some items which were
frequent at some point of time previously but are not seenin the recent transactions. Thus, there is a continuous upgradation of data. This makes working with such data difficult because tracks have to be maintained about the additionand deletion of items, and how this addition and deletion affects the itemsets already present.

3BASIC TERMINOLOGIES

3.1

Frequent Itemset Mining

Let X be a set of items, X = {x1 , x2 ,.....,xn }. Let T be set oftransactions that make the database. Each transaction in T is
an itemset, i.e., ∀ t ∈ T, t ⊆ X. With each transaction, a
unique identifier is associated, which is known as TID. Table
1 shows a sample database with 5 transactions.
TID

Itemsets

T1
T2
T3
T4
T5

ABE
BD
BC
ABD
ABCE

Figure 1: Block diagram for proposed work

Table 1: Sample Database T

3.2
Apriori algorithm[Agrawal et al., 1994] is used for frequent itemset mining from a set of transactions. The apriori algorithm proceeds by identifying individual items which
are frequent in the database, and then expanding the frequent
items to larger and larger sets, until the itemsets so formed
do not satisfy a minimum threshold value. This minimum
threshold value is known as minimum support.
Support indicates how often an itemset is seen in the
database. The support S of an itemset I with respect to T
can be defined as the number of transactions t in T in which I
occurs.
supp(I) =|t ∈ T ; t ⊆ I|
|T |

(1)

The confidence of an association rule, A =⇒ B is definedas the number of transactions that contain A that also containB.
conf (A =⇒ B) =supp(A ∪ B)
supp(A)(2)

Association rules generated after applying the apriori algorithm satisfy (i) a minimum threshold count, known as support, which is specified by the user, and (ii) a minimum confidence value.
When apriori is applied to the database in Table 1, witha minimum support count of 2(support of 40%) the frequent
itemsets are generated, as described in Table 2.
ItemsetSupport Count

AB
AE
BC
BD
BE
ABE

3
2
2
2
2
2

Table 2: Frequent Itemsets

Hiding Sensitive Itemsets

In collaborative data mining, when two or more companies
publish their data together, which can be further analysed,
there is a chance that some private information of a company
can be leaked by mining the frequent itemsets. Thus every
company hides some information which is sensitive to it, byusing privacy preserving data mining.
Let X be a set of items, X = {x1 , x2 ,.....,xn } which has to be
hidden from the database. With a given minimum support s,
the problem reduces to the transformation of the set of transactions T in the database to T’ such that the support of any
itemset xi < s, and | suppT (xi ) - suppT’ (xi ) | is minimum.

3.3

Period of Interest

For progressive data, a fixed sized sliding window is used.
This is also denoted as Period Of Interest(POI). The transactions which have timestamp falling into POI contribute tofrequent itemset mining. For a particular POI, frequent itemsets are found and are mapped to a buffer with their frequency
count and timestamp. Also, obsolete items are removed inparallel, as and when the buffer reaches its maximum capacity.

4

PROPOSED WORKThe flow diagram of the proposed work is represented by Figure 1. In the proposed approach, firstly the frequent itemsets are mined from the continuous stream of data by applying the apriori algorithm. Secondly, the sensitive itemsets that are supposed to be hidden, are masked by reducing the support count of victim items below the minimum
threshold. The database obtained after masking the sensitive
itemsets is known as sanitized database. Finally, the itemsets obtained thereafter are mapped onto a buffer with theirfrequency count and their timestamp of occurrence as shownin Figure 2. Further, the subsections discuss the steps of the
proposed approach in detail.

4.1

Frequent Pattern Generation

Apriori algorithm is applied for generating frequent patterns
from the continuous stream of transactions. The apriori algorithm takes a minimum threshold value as input. This value

of the minimum threshold specifies the minimum number oftimes an item has to be present in the transactions, so that is ismarked as a frequent item. The Apriori algorithm firstly finds
individual frequent items in the entire set of transactions, andthen it proceeds by forming sets of items called frequent patterns, until no further itemset can be found that satisfies theminimum support criteria.

4.2

Differentiating sensitive and non-sensitive
itemsets

The frequent patterns found by Apriori algorithm are further
divided into two sets: sensitive itemsets and non-sensitive
itemsets. This division is done on the basis of pre-defined sensitive patterns which is provided by the organisation whose
data has to be masked. If any sensitive pattern is found to
be a subset of a frequent pattern, then that particular frequent
pattern is added to the set of sensitive itemsets, otherwise thefrequent pattern is added to the set of non-sensitive itemsets.

4.3

Database Sanitization

Firstly, a victim item is to be chosen for each sensitive itemset. This paper proposes two methods for selecting victim
items for sensitive pattern hiding in progressive data:• TF-SProDmax(Time Fading based Sensitive patternhiding in Progressive Data max): In this method, theitem with maximum support is chosen as the victim
item.
• TF-SProDmin(Time Fading based Sensitive pattern hiding in Progressive Data min): In this method, the itemwith minimum support is chosen as the victim item.
After selecting the victim items, the support count of thesensitive itemsets is reduced below the minimum threshold.
This is done so that when frequent pattern mining is appliedon sanitized data, the sensitive patterns are never discoveredby anyone.

4.4

Figure 2: Adding frequent items to buffer

5

EXPERIMENTS AND DISCUSSIONSThe experiments were conducted to test the misses cost of
the sanitized dataset, and comparison has been made on thetwo proposed algorithms: TF-SProDmax and TF-SProDmin.
Also, to show the importance of timestamps, accuracy of TFSProD, which is a timestamp based method is compared withrespect to algorithm without timestamp. All the experiments
were performed using Python 3.7 on 64-bit Ubuntu machinewith 80 GiB RAM.

5.1

Simulation of progressive data

To simulate the effect of progressive data, the entire dataset
has been divided into chunks. Each chunk denotes a new incoming set of transactions at a particular point in time. Sensitive patern hiding is applied to ith chunk, then the sanitized
itemsets are stored in a buffer. The process is carried out similarly for i+1th to nth chunks.

5.2

Dataset Used

The datasets used for the experiments was generated by IBM
data generator. Different datasets are generated, and results
are varied according to the input parameters.
The datasets used have specifications as shown in Table 3

Adding sanitized items to buffer

Once the database is sanitized, a set of final frequent itemsets
is obtained. These frequent itemsets are to be added to thebuffer. The buffer contains a list of overall frequent itemswith their timestamp and frequency. The count is used todiscard less frequent(obsolete) items, once the buffer is full.
It may happen that an item has less frequency in the buffer
because it was added recently to the buffer. In such a case,timestamp is used so that an item can be deleted from thebuffer considering not only its frequency, but also the time atwhich it was added.
Figure 2 shows how the items are added to the buffer.
• If the item is already present in the buffer, its frequency
is incremented by 1, and its timestamp is updated to thecurrent timestamp.
• If the item is not present in the buffer, but the buffer has
space, then the item is simply added to the buffer withan initial frequency of 1.
• If the item is not present in the buffer, and the buffer isfull, the item with least frequency and oldest timestamp
is removed from the buffer.

DatasetNumber of Transactions

Chunk Size

Dataset 1
Dataset 2
Dataset 3
Dataset 41763243526487052961057944

400
400
400
400

Table 3: Dataset Configuration

5.3

Generation of sensitive itemsets

Since sensitive itemsets of an organization depend upon theinformation which is private to the organisation, they are notexternally generated by any algorithm, rather the sensitive information has to be provided by the organisation itself. Thus,
while hiding the sensitive itemsets, they have to be explicitly
specified. Since the data being used in this paper is synthetic,
the sensitive itemsets are also synthetically generated. Someof the frequent itemsets are randomly chosen from the entire
dataset and they are marked as sensitive itemsets.

Figure 3: Variation of misses ratio with number of sensitive items, keeping minimum support constant for different datasets

Figure 4: Variation of misses ratio with minimum support, keeping number of sensitive items constant for different datasets

5.4

Performance metrics

Let S be a set of sensitive itemsets, NSIoriginal be the non- sensitive frequent itemsets in the original database and NSIsanitized
be the non- sensitive frequent itemsets in sanitized database.
Misses cost is defined as the number of non-sensitive itemsets, that were mistakenly hidden by the algorithm. Misses
cost is defined by equation 3.
M C = |N SI original − N SI sanitized |(3)

Lesser the misses cost, the more efficient the algorithm is.
Misses ratio is the ratio of misses cost to NSIoriginal . Misses
ratio is defined by equation 4.
MR =

5.5

MC
N SI original(4)

Experimental Results

The Variation of misses ratio by changing the number ofsensitive items, keeping minimum support constant for different datasets is illustrated in Figure 3. The minimum support
was kept at 2%. As can be inferred from the graph, a generaltrend can be seen that by increasing the number of sensitive
items, there is an increase in the misses ratio. This can be explained as, the increase in the number of sensitive items will
be incurred at the cost of more and more items(victim items)
being removed from the transaction set, to reduce the countof sensitive items below minimum support. This may, in turn,mark some items as sensitive, which were not sensitive before
sanitization. Hence there is an increase in the misses ratio.
Also, it can be seen that TF-SProDmin performs better than
TF-SProDmax, in the sense that it has lower misses ratio that
TF-SProDmax, at all points. This can be explained as: sincein TF-SProDmin the item with minimum support is chosen
as the victim item, hence less number of transactions are to
be altered during the sanitization of the database. Thus, themisses ratio is less for TF-SProDmin.

Comparison of TF-SProdmax and TF-SProdmin
Experiments have been performed to analyze the effect of:• Choosing the element with the maximum support as thevictim item (TF-SProDmax)• Choosing the element with the minimum support as thevictim item (TF-SProDmin)
The experiment results are based on two types of variations:1. Finding misses ratio by keeping the support countconstant while varying the number of sensitive items.
[Fig. 3]
2. Finding misses ratio by keeping the number of sensitive
items constant while varying the support count. [Fig. 4]

Figure 5: Comparison of execution time for TF-SProDmax and TFSProDmin

Figure 6: Bar plots showing differences between accuracy of sensitive pattern hiding by using timestamp (TF-SProD) shown by blue bars
and without using timestamps shown by red bars for all the four datasets.

Figure 7: Execution time for various chunk sizes

Figure 8: Accuracy for various chunk sizes

Figure 4 shows the Variation of misses ratio by changing
the minimum support, keeping the number of sensitive items
constant for different datasets. The number of sensitive items
is kept as 84. It can be seen that with an increase in support count, the misses ratio decreases. This is because as
the support count is increased, fewer items qualify to be frequent items, and hence fewer transactions are altered during
database sanitization. Hence the misses ratio can be seen to
decrease gradually.
Again, it can be seen that TF-SProDmin performs better
than TF-SProDmax, in the sense that it has lower misses ratiothat TF-SProDmax, at all points, for the same reason as statedin above paragraph.
Figure 5 compares the execution time(in seconds) for TFSProDmin and TF-SProDmax. The support was kept at 2%,
the number of sensitive items was kept as 84, and the chunksize was 400. It can be seen that TF-SProDmin has slightly
less execution time for the same number of transactions. Thisis because, in TF-SProDmin, the item with minimum support
is chosen as victim item. Thus less number of transactions areto be altered to hide the sensitive patterns, hence the execution
time is less.

The accuracy is found out by comparing the items that werestored in the buffer after the last chunk was processed, with
their support count if the entire dataset was processed as asingle stream.
The accuracy of pattern hiding algorithm by using timestamps in the buffer(TF-SProD), as compared to pattern hiding
without using timestamp is shown in Figure 6. The numberof sensitive patterns were taken as 10. The minimum support
count was kept at 2%. The graph analyses the accuracy of
the two algorithms by varying the size (k) of the buffer fordifferent datasets. It can be seen that the accuracy of TFSProD outperforms the accuracy of pattern hiding without
timestamps. Particularly for small buffer sizes, the differencein the accuracy is very prominent. This is because for small
buffer sizes, items in the buffer have to be replaced quite frequently, as and when new frequent itemsets arrive. When we
use timestamp as a measure to remove the item, only obsolete items are deleted. But when we do not use timestamps
and use only support count to remove items from the buffer,
new items may be deleted from the dictionary, because they
were added recently and had very less support count. Thus
TF-SProD proves to be better in case of accuracy of the items.

Accuracy of TF-SProD
The accuracy of the proposed approach can be measured bycounting the number of itemsets that were stored in the buffer
at the end of the algorithm, and finding the number of items
that were actually frequent when the entire dataset is considered as one single stream of data. The accuracy is definedas:Accuracy =Number of items that were actually frequent
Total number of items in the buffer

Determining chunk size
The chunk value chosen for the experiments was 400. Figure 7 shows the execution time of TF-SProDmin for different
chunk sizes and Figure 8 shows the accuracy of TF-SProDmin
for various chunk sizes. From the figures, it is clear that therun time for chunk size 200 is maximum, but accuracy for thesame chunk is the best. Similarly for other chunk sizes, as we
increase the chunk size, the runtime reduces, but at the costof reduction of accuracy. Thus, a trade-off between accuracy

and chunk size is required, so that the algorithm can be runwith less execution time and good accuracy. For that purpose,the chunk size in the algorithms has been chosen as 400.

6

CONCLUSION

Most of the work in privacy preserving frequent pattern mining has been done on static data. However, most of thereal world data is progressive in nature. Thus, in this research work, two approaches for sensitive pattern hiding inprogressive data: TF-SProDmax and TF-SProDmin are proposed. Extensive experiments have been performed on synthetic datasets. The performance of TF-SProDmin is seen tobe better, both in terms of execution time and misses ratio.
To simulate a continuous flow of data, sliding window timefading model has been used, which keeps into account the frequency count of the frequent patterns, as well as their timestamp. The importance of timestamp can be realised as the
TF-SProD outperforms the algorithm without timestamp interms of accuracy. The timestamp ensures that a recently
added item with less frequency is not deleted, rather deletionof an item from the buffer is based on both frequency and its
time of addition into the buffer.
The research area of privacy preservation in progressive
data has great potential being an untouched area. A lot of
research can happen in this domain such as parallelization of
the algorithms using Hadoop MapReduce Framework, etc.

References[Aggarwal and Philip, 2008] Charu C Aggarwal and S Yu
Philip. Privacy-preserving data mining: models and algorithms. Springer Science & Business Media, 2008.
[Agrawal and Srikant, 2000] Rakesh Agrawal and Ramakrishnan Srikant. Privacy-preserving data mining. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pages 439–450, 2000.
[Agrawal et al., 1994] Rakesh Agrawal, Ramakrishnan
Srikant, et al. Fast algorithms for mining association
rules. In Proc. 20th int. conf. very large data bases,
VLDB, volume 1215, pages 487–499, 1994.
[Amiri, 2007] Ali Amiri. Dare to share: Protecting sensitive
knowledge with data sanitization. Decision Support Systems, 43(1):181–191, 2007.
[Cheng et al., 2016] Peng Cheng, John F Roddick, ShuChuan Chu, and Chun-Wei Lin. Privacy preservation
through a greedy, distortion-based rule-hiding method.
Applied Intelligence, 44(2):295–306, 2016.
[Fernandez-Basso et al., 2019] Carlos
Fernandez-Basso,
Abel J Francisco-Agra, Maria J Martin-Bautista, and
M Dolores Ruiz. Finding tendencies in streaming datausing big data frequent itemset mining. Knowledge-Based
Systems, 163:666–674, 2019.
[Jangra and Toshniwal, 2020] Shalini Jangra and Durga
Toshniwal. Vidpso: Victim item deletion based pso
inspired sensitive pattern hiding algorithm for dense
datasets.
Information Processing & Management,
57(5):102255, 2020.

[Lee et al., 2004] Guanling Lee, Chien-Yu Chang, and Arbee LP Chen. Hiding sensitive patterns in association rules
mining. In Proceedings of the 28th Annual International
Computer Software and Applications Conference, 2004.
COMPSAC 2004., pages 424–429. IEEE, 2004.
[Lin et al., 2015] Chun-Wei Lin, Tzung-Pei Hong, KuoTung Yang, and Shyue-Liang Wang. The ga-based algorithms for optimizing hiding sensitive itemsets throughtransaction deletion. Applied Intelligence, 42(2):210–230,
2015.
[Mhatre and Toshniwal, 2010] Amruta Mhatre and Durga
Toshniwal. Hiding co-occurring sensitive patterns inprogressive databases.
In Proceedings of the 2010
EDBT/ICDT Workshops, pages 1–5, 2010.
[Mhatre et al., 2009] Amruta Mhatre, Mridula Verma, andDurga Toshniwal. Extracting sequential patterns from progressive databases: A weighted approach. In 2009 International Conference on Signal Processing Systems, pages
788–792. IEEE, 2009.
[Oliveira and Zaiane, 2002] Stanley RM Oliveira and Osmar R Zaiane. Privacy preserving frequent itemset mining.
In Proceedings of the IEEE international conference on
Privacy, security and data mining-Volume 14, pages 43–
54. Australian Computer Society, Inc., 2002.
[Oliveira and Zaı̈ane, 2003] Stanley RM Oliveira and Osmar R Zaı̈ane. Protecting sensitive knowledge by data sanitization. In Third IEEE International Conference on Data
Mining, pages 613–616. IEEE, 2003.
[Radhakrishna et al., 2015] Vangipuram
Radhakrishna,
PV Kumar, and V Janaki. A survey on temporal databases
and data mining. In Proceedings of the The International
Conference on Engineering & MIS 2015, pages 1–6,
2015.
[Surendra and Mohan, 2019] H Surendra and HS Mohan.
Hiding sensitive itemsets without side effects. Applied Intelligence, 49(4):1213–1227, 2019.
[Thool and Voditel, 2013] Manisha Thool and Preeti Voditel.
Association rule generation in streams. International Journal of Advanced Research in Computer and Communication Engineering, 2(5), 2013.
[Verykios et al., 2004] Vassilios S Verykios, Ahmed K Elmagarmid, Elisa Bertino, Yücel Saygin, and Elena
Dasseni. Association rule hiding. IEEE Transactions on
knowledge and data engineering, 16(4):434–447, 2004.
[Yamamoto et al., 2019] Yoshitaka Yamamoto, Yasuo Tabei,
and Koji Iwanuma. Parasol: a hybrid approximation approach for scalable frequent itemset mining in streaming
data. Journal of Intelligent Information Systems, pages 1–
29, 2019.
[Yun and Lee, 2016] Unil Yun and Gangin Lee. Incremental mining of weighted maximal frequent itemsets fromdynamic databases. Expert Systems with Applications,
54:304–327, 2016.