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The question of how government agencies can acquire actionable, useful information about legitimate but unknown targets without intruding upon the electronic activity of innocent parties is extremely important. We address this question by providing experimental evidence that actionable, useful information can indeed be obtained in a manner that preserves the privacy of innocent parties and that holds government agencies accountable. In particular, we present practical, privacy-preserving protocols for two operations that law-enforcement and intelligence agencies have used effectively: set intersection and contact chaining. Experiments with our protocols suggest that privacy-preserving contact chaining can perform a 3-hop privacy-preserving graph traversal producing 27,000 ciphertexts in under two minutes. These ciphertexts are usable in turn via privacy-preserving set intersection to pinpoint potential unknown targets within a body of 150,000 total ciphertexts within 10 minutes, without exposing personal information about non-targets.
In this paper, we study the problem of summation evaluation of secrets. The secrets are distributed over a network of nodes that form a ring graph. Privacy-preserving iterative protocols for computing the sum of the secrets are proposed, which are co
In this paper, we present a general multiparty modeling paradigm with Privacy Preserving Principal Component Analysis (PPPCA) for horizontally partitioned data. PPPCA can accomplish multiparty cooperative execution of PCA under the premise of keeping
The Domain Name System (DNS) was created to resolve the IP addresses of the web servers to easily remembered names. When it was initially created, security was not a major concern; nowadays, this lack of inherent security and trust has exposed the gl
Differential privacy (DP) and local differential privacy (LPD) are frameworks to protect sensitive information in data collections. They are both based on obfuscation. In DP the noise is added to the result of queries on the dataset, whereas in LPD t
Releasing full data records is one of the most challenging problems in data privacy. On the one hand, many of the popular techniques such as data de-identification are problematic because of their dependence on the background knowledge of adversaries