$(\backslash$epsi $,\backslash$delta $)-$differential privacy $((\backslash$epsi $,\backslash$delta $)-$DP$)$ is a variant of differential privacy. A randomized query function satisfies $(\backslash$epsi $,\backslash$delta $)-$DP if$,$ for all data sets D$1,$ D$2$ such that one can be obtained from the other by modifying a single record, and all S $$ Range$($F $),$

Pr$($F $($D$1)$ in S$)<=$ exp$(\backslash$epsi $)\backslash$times Pr$($F $($D$2)$ in S$)+\backslash$delta $.$

For fixed $\backslash$epsi and $\backslash$delta $>0$ does $(\backslash$epsi $,\backslash$delta $)-$DP guarantee more privacy or less privacy than the standard $\backslash$epsi $-$differential privacy $(\backslash$epsi $-$DP$)$ defined in the course slides? How would you find $\backslash$epsi $$ such that $(\backslash$epsi $,\backslash$delta $)-$DP and $\backslash$epsi $-$DP give an equivalent level of privacy? Justify your answers.