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The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to certain “identifying” attributes) contains at least k records. Recently, several authors have recognized that k-anonymity cannot prevent attribute disclosure. The notion of l-diversity has been proposed to address this; `-diversity requires that each equivalence class has at least ` well-represented values for each sensitive attribute. In this article, we show that l-diversity has a number of limitations. In particular, it is neither necessary nor sufficient to prevent attribute disclosure. Motivated by these limitations, we propose a new notion of privacy called “closeness”. We first present the base model t- closeness, which requires that the distribution of a sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table (i.e., the distance between the two distributions should be no more than a threshold t). We then propose a more flexible privacy model called (n, t)-closeness that offers higher utility. We describe our desiderata for designing a distance measure between two probability distributions and present two distance measures. We discuss the rationale for using closeness as a privacy measure and illustrate its advantages through examples and experiments.
Doesn’t need to set security for your publishing data’s but yours are safe. Yes administrator only can see full details. The third party can’t fully details. Third searching for people in this records database can view splitting/blocking records using l-diversion and closeness.
L-diversion and closeness is derived formula can using secured data publishing. Here using the formula of l-diversion Entropy (E) = −∑s2S p(E, s) log p(E, s). Logically we will process this formula getting data’s recursively then splitting row wise data’s. For example Count that indicates the number of individuals. The probability of cancer among the population in the dataset is 700/3000 = 0.23 while the probability of cancer among individuals in the first equivalence class is as high as 300/600 = 0.5.
Doesn’t need to set security for your publishing data’s but yours are safe. Yes administrator only can see full details. The third party can’t fully details. Third searching for people in this records database can view splitting/blocking records using l-diversion and closeness.
Identity of indiscernible. An adversary has no information gain if her belief does not change. Mathematically, D[P,P] = 0, for any P.
Non-negativity: When the released data is available, the adversary has a non-negative information gain. Mathematically [P,Q] _ 0, for any P and Q.
Probability scaling: The belief change from probability_ to _+ is more significant than that from _ to _+ when _ < _ and _ is small. D[P,Q] should consider reflect the difference.
Zero-probability definability: D[P,Q] should be well-defined when there are zero probability values in P and Q.
This is one of property in Desiderata for Designing the Distance Measure. Searching for in particular is set on substring of asterisk (*). That mean the public visible data’s are blocked/substring in * symbols using EMD analyses. You can identify easy to see closeness ratio. L-diversion and closeness is very low the security mode very high. In case l-diversion and closeness is very high the security mode is very low.
K-Anonymity: If the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release. Example: If you try to identify a man from a release, but the only information you have is his birth date and gender. There are k people meet the requirement. This is k-Anonymity.
l-Diversity: Distinct l-diversity- Each equivalence class has at least l well-represented sensitive values. Entropy l-diversity- Each equivalence class not only must have enough different sensitive values, but also the different sensitive values must be distributed evenly enough. In the formal language of statistic, it means the entropy of the distribution of sensitive values in each equivalence class is at least log(l) Sometimes this maybe too restrictive. When some values are very common, the entropy of the entire table may be very low. This leads to the less conservative notion of l-diversity.
In this article, we propose a novel privacy notion called “closeness”. We first formalize the idea of global background knowledge and propose the base model t-closeness which requires that the distribution of a sensitive attribute in any equivalence class to be close to the distribution of the attribute in the overall table (i.e., the distance between the two distributions should be no more than a threshold t). This effectively limits the amount of individual-specific information an observer can learn. However, an analysis on data utility shows that t-closeness substantially limits the amount of useful information that can be extracted from the released data. Based on the analysis, we propose a more flexible privacy model called (n, t)-closeness, which requires the distribution in any equivalence class is close to the distribution in a large-enough equivalence class (contains at least n records) with respect to the sensitive attribute. This limits the amount of sensitive information about individuals while preserves features and patterns about large groups. Our analysis shows that (n, t)-closeness achieves a better balance between privacy and utility than existing privacy models such as l`-diversity and t-closeness.
While k-anonymity protects against identity disclosure, it does not provide sufficient protection against attribute disclosure. The notion of l`-diversity attempts to solve this problem. We have shown that l`-diversity has a number of limitations and especially presented two attacks on l`-diversity. Motivated by these limitations, we have proposed a novel privacy notion called “closeness”. We propose two instantiations: a base model called t-closeness and a more flexible privacy model called (n, t)-closeness.
We explain the rationale of the (n, t)- closeness model and show that it achieves a better balance between privacy and utility. To incorporate semantic distance, we choose to use the Earth Mover Distance measure. We also point out the limitations of EMD, present the desiderata for designing the distance measure, and propose a new distance measure that meets all the requirements. Finally, through experiments on real data, we show that similarity attacks are a real concern and the (n, t)- closeness model better protects the data while improving the utility of the released data. Below, we discuss some interesting open research issues.