$title Controlled Tabular Adjustments (CTA,SEQ=310) $onText Statistical agencies publish data which contains items that need to be altered to protect confidentiality. Controlled Tabular Adjustments (CTA) is a recent method to limit disclosure and can be elegantly expressed as a Mixed Integer Programming problem. The programming framework then allows easy expression of other data relationships like multi-dimensional adding up conditions. The following model uses a 3-dimensional table from from Cox, Kelly and Patil (2005) to illustrate this method. The data is stored in an Excel Spreadsheet. Lawrence H Cox, James P Kelly and Rahul J Patil, Computational Aspects of Controlled Tabular Adjustments: Algorithms and Analysis, in The Next Wave in Computing, Optimization, and Decision Technologies, Eds Bruce L Golden, S Raghavan and Edward A Wasil, Springer, 2005, pp 45-59. Keywords: mixed integer linear programming, statistical disclosure limitations $offText Set i 'rows' j 'columns' k 'planes'; Parameter dat(k<,i<,j<) 'unprotected data table' pro(k,i,j) 'information sensitive cells'; * extract data from Excel $onEmbeddedCode Connect: - ExcelReader: file: cox3.xlsx symbols: - name: dat range: Sheet1!A1 rowDimension: 2 columnDimension: 1 - name: pro range: Sheet2!A1 rowDimension: 2 columnDimension: 1 - GAMSWriter: symbols: all $offEmbeddedCode * do some basic data checks abort$sum((i,k), round(sum(j, dat(k,i,j)) - 2*dat(k,i,'total'))) 'row totals are incorrect', dat; abort$sum((j,k), round(sum(i, dat(k,i,j)) - 2*dat(k,'total',j))) 'column totals are incorrect', dat; abort$sum((i,j), round(sum(k, dat(k,i,j)) - 2*dat('total',i,j))) 'plane totals are incorrect', dat; Variable t(i,j,k) 'adjusted cell value' obj; Positive Variable adjN(i,j,k), adjP(i,j,k); Binary Variable b(i,j,k); Equation defadj(i,j,k) 'define new cell values' addrow(i,k) 'add up for rows' addcol(j,k) 'add up for columns' addpla(i,j) 'add up for plane' pmin(i,j,k) 'small value for sensitive cells' pmax(i,j,k) 'big value for sensitive cells' defobj; Set v(i,j,k) 'non zero cells' s(i,j,k) 'sensitive cells'; Parameter BigM 'the famous big M - make it as small as possible'; defadj(v(i,j,k)).. t(v) =e= dat(k,i,j) + adjP(v) - adjN(v); addrow(i,k).. sum(v(i,j,k), t(v)) =e= 2*t(i,'total',k); addcol(j,k).. sum(v(i,j,k), t(v)) =e= 2*t('total',j,k); addpla(i,j).. sum(v(i,j,k), t(v)) =e= 2*t(i,j,'total'); pmin(s(i,j,k)).. adjN(s) =g= pro(k,i,j)*(1 - b(s)); pmax(s(i,j,k)).. adjP(s) =g= pro(k,i,j)*b(s); Equation pminx, pmaxx; pminx(s(i,j,k)).. adjN(s) =l= BigM*pro(k,i,j)*(1 - b(s)); pmaxx(s(i,j,k)).. adjP(s) =l= BigM*pro(k,i,j)*b(s); defobj.. obj =e= sum(v, adjN(v) + adjP(v)); Model cox3 / all /; v(i,j,k) = dat(k,i,j); s(i,j,k) = pro(k,i,j); option limCol = 0, limRow = 0, solPrint = off, optCr = 0, optCa = 0.99, resLim = 10; BigM = 2; solve cox3 min obj using mip; Parameter rep(k,i,j) 'summary report' adjsum(k,i,j,*) 'adjustment summary' adjrep(k,i,j) 'adjustment report'; option rep:0:2:1, adjrep:0:2:1, adjsum:3:3:1; rep(k,i,j) = t.l(i,j,k); adjsum(k,i,j,'neg') = adjn.l(i,j,k); adjsum(k,i,j,'pos') = adjp.l(i,j,k); adjsum(k,i,j,'min') = pro(k,i,j); adjrep(k,i,j) = -adjN.l(i,j,k) + adjp.l(i,j,k); embeddedCode Connect: - GAMSReader: symbols: - name: adjrep - name: rep - name: adjsum - ExcelWriter: file: results.xlsx clearSheet: True symbols: - name: adjrep - name: rep - name: adjsum endEmbeddedCode * now we find the next best 5 solutions Set l 'solution labels' / solution1*solution5 / ll(l) 'dynamic version of l'; Parameter binrep(*,*,*,l) 'binary for protected variables' best 'best objective value'; option binrep:0:3:1; Equation cutone(l) 'cuts to exclude previous solutions' cuttwo(l) 'cuts to exclude previous solutions'; * there is always a complementary solution by just changing all the signs * cut(ll).. sum(s, abs(b(s) - binrep(s,ll)) =g= 1; cutone(ll).. sum(s$binrep(s,ll), 1 - b(s)) + sum(s$(not binrep(s,ll)), b(s)) =g= 1; cuttwo(ll).. sum(s$(not binrep(s,ll)), 1 - b(s)) + sum(s$binrep(s,ll), b(s)) =g= 1; Model cox3c 'includes cuts' / all /; * find the card(l) best solutions that are within 1% of the global best = round(obj.l); cox3c.resUsd = cox3.resUsd; cox3c.nodUsd = cox3.nodUsd; loop(l$((obj.l - best)/best <= 0.01), ll(l) = yes; binrep(s,l) = round(b.l(s)); binrep('','','Obj',l) = obj.l; binrep('','','mSec',l) = cox3c.resUsd*1000; binrep('','','nodes',l) = cox3c.nodUsd; binrep('Comp','Cells','Adjusted',l) = sum((i,j,k)$(not s(i,j,k)), 1$round(adjn.l(i,j,k) + adjp.l(i,j,k))); solve cox3c min obj using mip; ); embeddedCode Connect: - GAMSReader: symbols: - name: binrep - ExcelWriter: file: results.xlsx clearSheet: True symbols: - name: binrep endEmbeddedCode