Distributed Query Count using Non-Cooperating Parties

In homomorpheR: Homomorphic Computations in R

### get knitr just the way we like it

knitr::opts_chunk$set(
  message = FALSE,
  warning = FALSE,
  error = FALSE,
  tidy = FALSE,
  cache = FALSE
  )
library(homomorpheR)
if (!require("magrittr", quietly = TRUE)) stop("This vignette requires the magrittr package!")
if (!require("dplyr", quietly = TRUE)) stop("This vignette requires the dplyr package!")

Introduction

We demonstrate the use of non-cooperating parties to run a distributed query count computation using the homomorpheR package a simulated data set containing:

• sex (F, M) for female/male
• age between 40 and 70
• bm a biomarker

set.seed(130)
sample_size  <- c(60, 15, 25)
query_data  <- local({
    tmp  <- c(0, cumsum(sample_size))
    start  <- tmp[1:3] + 1
    end  <- tmp[-1]
    id_list  <- Map(seq, from = start, to = end)
    lapply(seq_along(sample_size),
           function(i) {
               id  <- sprintf("P%4d", id_list[[i]])
               sex <- sample(c("F", "M"), size = sample_size[i], replace = TRUE)
               age <- sample(40:70, size = sample_size[i], replace = TRUE)
               bm <- rnorm(sample_size[i])
               data.frame(id = id, sex = sex, age = age, bm = bm, stringsAsFactors = FALSE)
           })
})

Site 1

str(query_data[[1]])

Site 2

str(query_data[[2]])

Site 3

str(query_data[[3]])

Aggregated Query

If the data were all aggregated in one place, it would very simple to query it. Let us run a sample query on this aggregated data set for the condition age < 50 & sex == 'F' & bm < 0.2

do.call(rbind, query_data) %>%
    filter(age < 50 & sex == 'F' & bm < 0.2) %>%
    nrow()

Distributed Computation

Assume now that the data query_data is distributed between three sites none of whom want to share actual data among each other or even with a master computation process. They wish to keep their data secret but are willing, together, to provide the sum of the total count. They wish to do this in a manner so that the master process is unable to associate the contribution to the likelihood from each site.

The overall query count for for the entire data is the sum of the counts at each site. How can this count be computed while preventing the master from knowing the individual contributions of each site?

We will use two non-cooperating parties, say NCP1 and NCP2, to accomplish this. These parties do not talk to each other, but do talk to the sites and the master process. Site $i$ sends $E(c_i + r_i)$ to NCP1 and $E(c_i - r_i)$ to NCP2, where $c_i$ is the actual count, $E(c_i)$ denotes the encrypted value of $c_i$ and $r_i$ is a random quantity generated anew for each site. NCP1 can compute $\sum_{i=1}^3E(c_i + r_i)$ and NCP2 can compute $\sum_{i=1}^3E(c_i - r_i)$, but individually, neither has a handle on $l = \sum_{i=1}^3 c_i$.

The master process can retrieve $\sum_{i=1}^3E(c_i + r_i)$ and $\sum_{i=1}^3E(c_i - r_i)$ from NCP1 and NCP2 respectively. Each is an encrypted value of the sum of counts from all sites, obfuscated by random terms, and hence is random to the master. However, the master using the associative and homomorphic properties of $E(.)$, can compute:

[ \sum_{i=1}^3E(c_i + r_i) +\sum_{i=1}^3E(c_i - r_i) = \sum_{i=1}^3E(c_i + r_i + c_i - r_i) = \sum_{i=1}^3E(2c_i) = E(2c) ]

since $c = c_1 + c_2 + c_3$ is the grant total count. The master can now decrypt the result and obtain $c$!

This is pictorially shown below.

The red arrows show the master proposing a value $\beta$ to each of the sites, which reply back to NCP1 and NCP2. The master then retrieves the values from NCP1 and NCP2 and sums them.

A Modified Topology

The drawback of the above scheme is that channels of communication have to be established from each site to the master process and also to the two non-cooperating parties NCP1 and NCP2. If the number of participating sites in a computation changes, then both the master and NCP1 and NCP2 have to be made aware of the change.

It would be simpler if only NCP1 and NCP2 can talk to both the master and the sites. Such a situation would arise, for example, when the sites are all participating in a disease specific registry. The parties NCP1 and NCP2 would probably be set up once and any new site that has to be onboarded needs only to be known to P1 and P2. This has the added advantage of hiding the number of sites, which could even be 1!

Such a communication topology would mean that the $\beta$ values have be funneled to the sites through NCP1 and NCP2 and that can be easily accomplished. The picture below shows this configuration and looks more complicated than it actually is.

To summarize, the modified scheme has several characteristics:

• The master only communicates with NCP1 and NCP2
• NCP1 and NCP2 are the only parties communicating with both the master and sites
• NCP1 and NCP2 are the only ones that know how many sites are participating
• New sites can be added and only NCP1 and NCP2 need to account for them while the master remains oblivous to the number of sites; so the scheme works even with one site
• It appears that there is unnecessary communication of the same information, i.e. $\beta$ is being sent twice to each site from each of the NCP1 and NCP2. This is easily mitigated by engineering either by using a broker between NCP1 and NCP2, or the sites caching their results for a short period to avoid recomputation.

Implementation

The above implementation assumes that the encryption and decryption can happen with real numbers which is not the actual situation. Instead, we use rational approximations using a large denominator, $2^{256}$, say. In the future, of course, we need to build an actual library is built with rigorous algorithms guaranteeing precision and overflow/undeflow detection. For now, this is just an ad hoc implementation.

Also, since we are only using homomorphic additive properties, a partial homomorphic scheme such as the Paillier Encryption system will be sufficient for our computations.

We define classes to encapsulate our sites, non-cooperating parties and a master process.

The Site Class

Our site class will compute the count on site data.

Site <-
    R6::R6Class(
            "Site",
            private = list(
                ## name of the site
                name = NA,
                ## local data
                data = NA,
                result_cache = NULL,
                filterCondition = NA,
                local_query_count = function() {
                    ## Check if value is cached
                    result  <- private$result_cache
                    if (is.null(result)) {
                        ## We need to run the query
                        pubkey <- self$pubkey
                        ## Generate random offset for int and frac parts
                        offset.int <- random.bigz(nBits = 256)
                        ## 2. Add to count
                        data  <- private$data
                        filter_expr  <- eval(parse(text = paste("rlang::expr(", private$filterCondition, ")")))
                        data %>%
                            dplyr::filter(!! filter_expr) %>%
                            nrow() ->
                            result.int
                        result  <- list(
                            int1 = pubkey$encrypt(result.int - offset.int),
                            int2 = pubkey$encrypt(result.int + offset.int)
                        )
                        private$result_cache  <- result
                    }
                    result
                }
            ),
            public = list(
                ## Common denominator for approximate real arithmetic
                den = NA,
                ## The master's public key; everyone has this
                pubkey = NA,
                initialize = function(name, data) {
                    private$name <- name
                    private$data <- data
                },
                setPublicKey = function(pubkey) {
                    self$pubkey <- pubkey
                },
                setDenominator = function(den) {
                    self$den = den
                },
                setFilterCondition = function (filterCondition) {
                    private$filterCondition  <- filterCondition
                },
                ## query count,
                query_count = function(party) {
                    result  <- private$local_query_count()
                    if (party == 1) result$int1 else result$int2
                }
            )
        )

The Non-cooperating Parties Class

The non-cooperating parties can communicate with the sites. So they have methods for adding sites, passing on public keys from the master etc. The query_count method for this class merely calls each site to compute the result and adds them up before sending it on to the master, so that the master has no idea of the individual contributions.

NCParty <-
    R6::R6Class(
            "NCParty",
            private = list(
                ## name of the site
                name = NA,
                ## NC party number
                number = NA,
                ## filter condition
                filterCondition = NA,
                ## The master
                master = NA,
                ## The sites
                sites = list()
            ),
            public = list(
                ## The master's public key; everyone has this
                pubkey = NA,
                ## The denoinator for rational arithmetic
                den = NA,
                initialize = function(name, number) {
                    private$name <- name
                    private$number  <- number
                },
                setPublicKey = function(pubkey) {
                    self$pubkey <- pubkey
                    ## Propagate to sites
                    for (site in sites) {
                        site$setPublicKey(pubkey)
                    }
                },
                setDenominator = function(den) {
                    self$den <- den
                    ## Propagate to sites
                    for (site in sites) {
                        site$setDenominator(den)
                    }
                },
                setFilterCondition = function(filterCondition) {
                    private$filterCondition  <- filterCondition
                    ## Propagate to sites
                    for (site in sites) {
                        site$setFilterCondition(filterCondition)
                    }
                },
                addSite = function(site) {
                    private$sites  <- c(private$sites, list(site))
                },
                ## sum of all counts
                query_count = function() {
                    pubkey  <- self$pubkey
                    results  <- lapply(sites, function(x) x$query_count(private$number))
                    ## Accumulate the integer and fractional parts
                    n  <- length(results)
                    enc_sum <- pubkey$encrypt(0)
                    for (result in results) {
                        enc_sum  <- pubkey$add(enc_sum, result)
                    }
                    enc_sum
                }
            )
        )

The Master Class

The master process

Master  <-
    R6::R6Class(
            "Master",
            private = list(
                ## name of the site
                name = NA,
                ## Private and public keys
                keys = NA,
                ## Non cooperating party 1
                nc_party_1 = NA,
                ## Non cooperating party 2
                nc_party_2 = NA,
                ## filter condition
                filterCondition = NA
            ),
            public = list(
                ## Denominator for rational arithmetic
                den  = NA,
                initialize = function(name, filterCondition) {
                    private$name <- name
                    private$keys <- PaillierKeyPair$new(1024) ## Generate new public and private key.
                    self$den <- gmp::as.bigq(2)^256  #Our denominator for rational approximations
                    private$filterCondition  <- filterCondition
                },
                setNCParty1  = function(site) {
                    private$nc_party_1 <- site
                    private$nc_party_1$setPublicKey(private$keys$pubkey)
                    private$nc_party_1$setDenominator(self$den)
                    private$nc_party_1$setFilterCondition(private$filterCondition)
                },
                setNCParty2  = function(site) {
                    private$nc_party_2 <- site
                    private$nc_party_2$setPublicKey(private$keys$pubkey)
                    private$nc_party_2$setDenominator(self$den)
                    private$nc_party_2$setFilterCondition(private$filterCondition)
                },
                ## Query count
                query_count = function() {
                    pubkey  <- private$keys$pubkey
                    privkey  <- private$keys$getPrivateKey()
                    result1  <- private$nc_party_1$query_count()
                    result2  <- private$nc_party_2$query_count()
                    ## Sum it
                    enc_sum <- pubkey$add(result1, result2)
                    final_result <- as.integer(privkey$decrypt(enc_sum))
                    ## Since we 2c, we divide by 2.
                    final_result / 2
                }
            )
        )

Example

We are now ready to use our sites in the computation.

1. Create sites

site1 <- Site$new(name = "Site 1", data = query_data[[1]])
site2 <- Site$new(name = "Site 2", data = query_data[[2]])
site3 <- Site$new(name = "Site 3", data = query_data[[3]])

sites  <- list(site1 = site1, site2 = site2, site3 = site3)

2. Create Non-cooperating parties

ncp1  <- NCParty$new("NCP1", 1)
ncp2  <- NCParty$new("NCP1", 2)

We add sites to the non-cooperating parties.

for (s in sites) {
    ncp1$addSite(s)
    ncp2$addSite(s)
}

3. Create the master process

master  <- Master$new(name = "Master",
                      filterCondition = "age < 50 & sex == 'F' & bm < 0.2")

We next connect the master to the non-cooperating parties.

master$setNCParty1(ncp1)
master$setNCParty2(ncp2)

At this point the communication graph has been defined between the master and non-cooperating parties and the non-cooperating parties and the sites.

4. Perform the Query

cat(sprintf("Query Count is %d\n", master$query_count()))

References

homomorpheR documentation built on April 11, 2025, 6:15 p.m.