Secure multiparty sum computation corruption bound In Section 3.4 of the book Secure Multiparty Computation and Secret Sharing, it is claimed that for a secure multiparty computation problem with $n$ parties, the optimal corruption bound (concerning passive adversaries and information-theoretic security) is $n/2$, that is, at most $n/2$ passive adversaries are allowed in order to have information-theoretic security. It is claimed that this bound is optimal because there exist functions that cannot be computed securely if there are $\geq n/2$ adversaries. In particular, in the first paragraph of Section 3.4 of the book, there is one sentence: the multiplication subprotocol breaks down. The book and other papers provide analysis for Boolean functions, e.g., AND, OR and XOR functions. My question is: What is the corruption bound for secure multiparty integer sum computations? More specifically, given $n$ parties $P_1,\cdots,P_n$, each $P_i$ holds a secret integer $x_i$ (assuming that all $x_i$'s are inside a finite field $\mathbb{F}$) and they want to securely compute $S=\sum_{i=1}^nx_i$. Inside these $n$ parties, $t$ of them could be semi-honest adversaries and we are concerned with information-theoretic security. Also assuming complete communication graph. I am trying to apply the Shamir's secret sharing scheme. For this problem, is the corruption bound $t&lt;n/2$ or $t&lt;n$?
For just computing a sum, the information-theoretic bound (assuming semihonest) is $t&lt;n-1$.

Why $n-1$ and not $n$?
In the case of $t&lt;n$, we can allegedly tolerate up to $n-1$ corrupt parties. If we are computing a sum, and the $n-1$ parties know the sum $S$ and their own inputs ($x_i$), they can simply compute the sum of their inputs and subtract that from $S$ to learn the private input of the single honest party. Therefore, there must be at least two honest parties if we are computing the sum.