In this work, we discuss our successful efforts for industry deployment of a cryptographic secure computation protocol. The problem we consider is privately computing aggregate conversion rate of advertising campaigns. This underlying functionality can be abstracted as Private Intersection-Sum (PI-Sum) with Cardinality. In this setting two parties hold datasets containing user identifiers, and one of the parties additionally has an integer value associated with each of its user identifiers. The parties want to learn the number of identifiers they have in common and the sum of the integer values associated with these users without revealing any more information about their private inputs. We identify the major properties and enabling factors which make the deployment of a cryptographic protocol possible, practical, and uniquely positioned as a solution for the task at hand. We describe our deployment setting and the most relevant efficiency measure, which in our setting is communication overhead rather than computation. We also present a monetary cost model that can be used as a unifying cost measure and the computation model which reflect out use-case: a low-priority batch computing. We present three PI-Sum with cardinality protocols: our currently deployed protocol, which relies on a Diffie-Hellman style double masking, and two new protocols which leverage more recent techniques for private set intersection (PSI) that use Random Oblivious Transfer and encrypted Bloom filters. We compare the later two protocol with our original solution when instantiated with different additively homomorphic encryption schemes. We implement our constructions and compare their costs. We also compare with recent generic approaches for computing on the intersection of two datasets and show that our best protocol has monetary cost that is 20× less than the best known generic approach.

We introduce operations to safely update an anatomized database. The result is a database where the view of the server satisfies standards such as k-anonymity or l-diversity, but the client is able to query and modify the original data. By exposing data where possible, the server can perform value-added services such as data analysis not possible with fully encrypted data, while still being unable to violate privacy constraints. Update is a key challenge with this model; naïve application of insertion and deletion operations reveals the actual data to the server. This paper shows how data can be safely inserted, deleted, and updated. The key ideas are that data is inserted or updated into an encrypted temporary table until enough data is available to safely decrypt, and that sensitive information of deleted tuples is left behind to ensure privacy of both deleted and undeleted individuals. This approach is proven effective in maintaining the privacy constraint against an adversarial server. The paper also gives empirical results on how much data remains encrypted, and the resulting quality of the server's (anatomized) view of the data, for various update and delete rates.

Oblivious Transfer (OT) is an important cryptographic tool, which has found its usage in many crypto protocols, such as Secure Multiparty Computations, Certified E-mail and Simultaneous Contract Signing . In this paper, we propose three k-out-of-n OT (OT_k^n) protocols based on additive homomorphic encryption. Two of these protocols prohibit malicious behaviors from a receiver. We also achieve efficient communication complexity bounded by O(l* n) in bits, where l is the size of the encryption key. The computational complexity is comparable to the most efficient existing protocols. Due to the semantic security property, the sender cannot get receiver's selection. When the receiver tries to retrieve more than k values, the receiver is caught cheating with 1-(1/m) probability (Protocol II) or the receiver is unable to get any value at all (Protocol III). We introduce a novel technique based on the solvability of linear equations, which could find its way into other applications. We also provide an experimental analysis to compare the efficiency of the protocols.

k-Anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous data set, any identifying information occurs in at least k tuples. Much research has been done to modify a single-table data set to satisfy anonymity constraints. This paper extends the definitions of k-anonymity to multiple relations and shows that previously proposed methodologies either fail to protect privacy or overly reduce the utility of the data in a multiple relation setting. We also propose two new clustering algorithms to achieve multirelational anonymity. Experiments show the effectiveness of the approach in terms of utility and efficiency.

k-anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous dataset, any identifying information occurs in at least k tuples. Much research has been done to modify a single table dataset to satisfy anonymity constraints. This paper extends the definitions of k-anonymity to multiple relations and shows that previously proposed methodologies either fail to protect privacy, or overly reduce the utility of the data, in a multiple relation setting. A new clustering algorithm is proposed to achieve multirelational anonymity.