def protocol():
# 3 columns for party 1
a = create_column("a", "INTEGER")
b = create_column("b", "INTEGER")
c = create_column("c", "INTEGER")
# 3 columns for party 2
d = create_column("d", "INTEGER")
e = create_column("e", "INTEGER")
f = create_column("f", "INTEGER")
# 3 columns for party 3
g = create_column("g", "INTEGER")
h = create_column("h", "INTEGER")
i = create_column("i", "INTEGER")
# create all input relations
rel_one = lang.create("in1", [a, b, c], {1})
rel_two = lang.create("in2", [d, e, f], {2})
rel_three = lang.create("in3", [g, h, i], {3})
# concatenate input relations
cc = lang.concat([rel_one, rel_two, rel_three], "cc")
# compute deciles of values in column "a"
agg = lang.deciles(cc, "dec_out", [], "a")
# collect output
lang.collect(agg, {1, 2, 3})
# return the workflow's root nodes
return {rel_one, rel_two, rel_three}
def protocol():
# 3 columns for input dataset (shared between parties {1, 2, 3}
a = create_column("a", "INTEGER")
b = create_column("b", "INTEGER")
c = create_column("c", "INTEGER")
# create shared input relation
rel_one = lang.create("inpt", [a, b, c], {1, 2, 3})
# simple sum aggregation over column "a", with no key columns
agg = lang.aggregate(rel_one, "agg", None, "a", "sum")
# reveal output to parties 1, 2, and 3
lang.collect(agg, {1, 2, 3})
# return the workflow's root node
return {rel_one}
def protocol():
# 3 columns for input dataset (shared between parties {1, 2, 3}
a = create_column("a", "INTEGER")
b = create_column("b", "INTEGER")
c = create_column("c", "INTEGER")
# create shared input relation
rel_one = lang.create("inpt", [a, b, c], {1, 2, 3})
# compute deciles of values in column "a"
d = lang.deciles(rel_one, "deciles", [], "a")
# reveal output to parties 1, 2, and 3
lang.collect(d, {1, 2, 3})
# return the workflow's root node
return {rel_one}
def protocol():
# 3 columns for party 1
a = create_column("a", "INTEGER")
b = create_column("b", "INTEGER")
c = create_column("c", "INTEGER")
# 3 columns for party 2
d = create_column("d", "INTEGER")
e = create_column("e", "INTEGER")
f = create_column("f", "INTEGER")
# 3 columns for party 3
g = create_column("g", "INTEGER")
h = create_column("h", "INTEGER")
i = create_column("i", "INTEGER")
# create all input relations
rel_one = lang.create("in1", [a, b, c], {1})
rel_two = lang.create("in2", [d, e, f], {2})
rel_three = lang.create("in3", [g, h, i], {3})
# concatenate input relations
cc = lang.concat([rel_one, rel_two, rel_three], "cc")
# simple sum aggregation over column "a", grouped by the values
# from column "b".
# note that since we didn't explicitly pass column names to concat(),
# the columns in its output relation were named according to the first
# relation in its input (here, relation rel_one).
agg = lang.aggregate(cc, "agg", ["b"], "a", "sum")
# reveal output to parties 1, 2, and 3
lang.collect(agg, {1, 2, 3})
# return the workflow's root nodes
return {rel_one, rel_two, rel_three}