Class

ksb.csle.didentification.verification.loss

InformationLoss

Related Doc: package loss

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abstract class InformationLoss extends Serializable

This abstract class defines some functions for measuring information loss. To Be Modified for future

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Instance Constructors

  1. new InformationLoss()

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Abstract Value Members

  1. abstract def lossMeasure(src: DataFrame, anonymizedSrc: DataFrame, suppressedSrc: DataFrame, columnNames: Array[String]): InformationLossBound

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    Measures the information loss of anonymized data compared to the original data.

    Measures the information loss of anonymized data compared to the original data.

    src

    the dataframe

    anonymizedSrc

    the anonymized dataframe

    suppressedSrc

    the suppressed dataframe

    columnNames

    the array of column names

    returns

    InformationLossBound the measured information loss bound

Concrete Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. def convertColumntoArray(src: DataFrame, columnName: String): Array[String]

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    Converts all the contents of a specific column to the array of string.

    Converts all the contents of a specific column to the array of string.

    src

    the dataframe

    columnName

    the column name

    returns

    Array[String] the number of equivalence classes

  7. final def eq(arg0: AnyRef): Boolean

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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  10. final def getClass(): Class[_]

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  11. def getNumEquivalence(src: DataFrame, columnNames: Array[String]): Double

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    Gets the number of the equivalence class which is composed of referring to the columns of quasi-identifiers.

    Gets the number of the equivalence class which is composed of referring to the columns of quasi-identifiers.

    src

    the dataframe

    columnNames

    the array of quasi-identifier columns

    returns

    Long the number of equivalence classes

  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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  14. def lossMeasure(src: DataFrame, anonymizedSrc: DataFrame, columnNames: Array[String]): InformationLossBound

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    Measures the information loss of anonymized data compared to the original data.

    Measures the information loss of anonymized data compared to the original data.

    src

    the dataframe

    anonymizedSrc

    the anonymized dataframe

    columnNames

    the array of column names

    returns

    InformationLossBound the measured information loss bound

  15. final def ne(arg0: AnyRef): Boolean

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. final def synchronized[T0](arg0: ⇒ T0): T0

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  19. def toString(): String

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  20. final def wait(): Unit

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  21. final def wait(arg0: Long, arg1: Int): Unit

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  22. final def wait(arg0: Long): Unit

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