### Table 4.1: Three private set protocols compared in different security settings. ROM stands for Random Oracle Model , NIZK for Non-Interactive Zero Knowledge , and UC for Universally Composable .

2006

### Table 6: Non-interactive ad hoc trials

1995

"... In PAGE 12: ... 4 In a few cases this resulted in the #0Cnal query containing less than the speci#0Ced number of terms. 5 Note that the average precision, R#7Bprecision and recall #0Cgures in Table6 are arti#0Ccially low because we used the full set of disks... ..."

Cited by 83

### Table 6.4: Workload for non-interactive applications

1999

Cited by 1

### Table 6 shows the running time for the three non-interactive

### Table 3: Statistics for the number of pairs of non-interacting requests over 30 random and optimal permutations.

2004

"... In PAGE 4: ... We also compute the average number of pairs of non-interacting requests over 30 permutations evaluated as best known values by Genitor. The results of the pairwise sensitivity test are summarized in Table3 . The second column Total Pairs represents the total number of request pairs in the permutation (if there are n requests, the number in this column is n(n 1)=2).... ..."

Cited by 6

### Table 3: Statistics for the number of pairs of non-interacting requests over 30 random and optimal permutations.

2004

"... In PAGE 4: ... We also compute the average number of pairs of non-interacting requests over 30 permutations evaluated as best known values by Genitor. The results of the pairwise sensitivity test are summarized in Table3 . The second column Total Pairs represents the total number of request pairs in the permutation (if there are D2 requests, the number in this column is D2B4D2 A0 BDB5BPBE).... ..."

Cited by 6

### Table 3: Statistics for the number of pairs of non-interacting requests over 30 random and optimal permutations.

2004

"... In PAGE 4: ... We also compute the average number of pairs of non-interacting requests over 30 permutations evaluated as best known values by Genitor. The results of the pairwise sensitivity test are summarized in Table3 . The second column Total Pairs represents the total number of request pairs in the permutation (if there are a1 requests, the number in this column is a1 a32a5 a1 a8 a5 a38a10a6a5a8a7 ).... ..."

Cited by 6

### Table 8: Odds ratios for non-interacted variables. ENTRY MODEL EXIT MODEL

2006

"... In PAGE 24: ... Table 9 report respectively the odds ratios of the interacted variables for non LCC (D_LCC = 0 and hence, ORk = Exp[ k]) and for LCC (D_LCC = 1 and hence, ORk = Exp[ k + k]). It is straightforward to show that if Xk is a non-interacted variable, its odds ratio will be: ORk = Exp[ k] Table8 reports the odds ratios for non-interacted variables. Finally, the odds ratio of the dummy variable D_LCC can be written as: ORk = Exp[ 0 + Pm k=1 kXk + Pn k=1 kXk] Exp[Pn k=1 kXk] which yields: ORD_LCC = Exp quot; 0 + m X k=1 kXk # The interpretation of the odds ratio for the dummy variable D_LCC is somehow trickier, as it also comprises the m interacted variables.... ..."

### Table 2: Benchmark results for tracing some real world (non-interactive) applications.

2003

"... In PAGE 3: ... We also benchmarked some standard non- interactive applications which are closer to re- alistic (interactive) applications than the micro- benchmarks. The results of these tests have been summarized in Table2 . When taking a closer look at these numbers one notices that the slowdown for most benchmarks is lower than a factor of 2.... ..."

Cited by 5

### Table 1: The change of sensitivities and specificities by the ratios of interacting to non-interacting sets of protein pairs in training sets.

in Genome Informatics 15(2): 171--180 (2004) 171 PreSPI: Design and Implementation of Protein-Protein

"... In PAGE 4: ... Note that the protein pairs without overlapping domains in AP matrix are not included in the test data in the measurement. Table1 shows the sensitivities and specificities of each test group depending on the ratios of interacting and non-interacting set of protein pairs. The data in each test group is divided further into two subgroups; one group is the test set of protein pairs which has a matching PIP value in PIP distributions and the other group is the test set of protein pairs without matching PIP value in PIP distribution.... In PAGE 4: ... The data in each test group is divided further into two subgroups; one group is the test set of protein pairs which has a matching PIP value in PIP distributions and the other group is the test set of protein pairs without matching PIP value in PIP distribution. As shown in Table1 , very high sensitivities and specificities were achieved for the test groups with matching PIP values, whereas moderate sensitivities and specificities were achieved for the test groups without matching PIP values. In the test, it was revealed that protein pairs with common domains in AP matrix are amenable to have matching PIP values in the PIP distributions.... ..."