A new model for privacy preserving sensitive Data Mining

Prakash, M.; Singaravel, G.

doi:10.1109/icccnt.2012.6396017

Cited by 14 publications

(4 citation statements)

References 4 publications

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“…The fourth phase is concerned with data hiding; whether it is the raw or aggregate data that need to be hid. Data hiding refers to the cases where the sensitive data from original database like identity, name, and address that can be linked, directly or indirectly, to an individual person are hided [14][15][16]. The last phase is the privacy preservation techniques.…”

Section: The Privacy Frameworkmentioning

confidence: 99%

An approach for prevention of privacy breach and information leakage in sensitive data mining

Prakash

Singaravel

2015

Computers & Electrical Engineering

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Section: The Privacy Frameworkmentioning

confidence: 99%

An approach for prevention of privacy breach and information leakage in sensitive data mining

Prakash

Singaravel

2015

Computers & Electrical Engineering

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“…One of the major threats people face today is Cyber Crime [4]. Since most of our information is stored on electronic media and a lot of data is also available on internet or networks.…”

Section: A Cyber Terrorism Insider Threats and External Attacksmentioning

confidence: 99%

“…Another area which requires attention is detecting frauds and thefts. Frauds may be credit card frauds [4]. These can be detected by identifying purchases made of enormous amounts.…”

Section: B Credit Card Fraud and Identity Theftmentioning

confidence: 99%

Review: Privacy Preserving and Sensitive Data Hiding Methods

Tomar¹,

Singh²

2015

NN-CSE

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Privacy preserving data mining deals with hiding an individual’s sensitive identity without sacrificing the usability of data. It has become a very important area of concern but still this branch of research is in its infancy .People today have become well aware of the privacy intrusions of their sensitive data and are very reluctant to share their information. The major area of concern is that non-sensitive data even may deliver sensitive information, including personal information, facts or patterns. Several techniques of privacy preserving data mining have been proposed in literature.

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“…A lot of excellent works [1][2][3][4][5] have been conducted to protect users' privacy in statistic data. Among all these works, the state-of-the-art privacy model is differential privacy [5], which can provide rigorous privacy guarantees.…”

Section: Introductionmentioning

confidence: 99%

Differentially Private Web Browsing Trajectory over Infinite Streams

Liu

Guo

Tan

et al. 2021

Security and Communication Networks

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Nowadays, a lot of data mining applications, such as web traffic analysis and content popularity prediction, leverage users’ web browsing trajectories to improve their performance. However, the disclosure of web browsing trajectory is the most prominent issue. A novel privacy model, named Differential Privacy, is used to rigorously protect user’s privacy. Some works have applied this privacy model to spatial-temporal streams. However, these works either protect the users’ activities in different places separately or protect their activities in all places jointly. The former one cannot protect trajectories that traverse multiple places; while the latter ignores the differences among places and suffers the degradation of data utility (i.e., data accuracy). In this paper, we propose a w , n -differential privacy to protect any spatial-temporal sequence occurring in w successive timestamps and n -range places. To achieve better data utility, we propose two implementation algorithms, named Spatial-Temporal Budget Distribution (STBD) and Spatial-Temporal RescueDP (STR). Theoretical analysis and experimental results show that these two algorithms can achieve a balance between data utility and trajectory privacy guarantee.

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A new model for privacy preserving sensitive Data Mining

Cited by 14 publications

References 4 publications

An approach for prevention of privacy breach and information leakage in sensitive data mining

An approach for prevention of privacy breach and information leakage in sensitive data mining

Review: Privacy Preserving and Sensitive Data Hiding Methods

Differentially Private Web Browsing Trajectory over Infinite Streams

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