Optimal Contracts for Outsourced Computation

While expensive cryptographically verifiable computation aims at defeating malicious agents, many civil purposes of outsourced computation tolerate a weaker notion of security, i.e., “lazy-but-honest” contractors. Targeting this type of agents, we develop optimal contracts for outsourcing of computational tasks via appropriate use of rewards, punishments, auditing rate, and “redundancy”. Our contracts provably minimize the expense of the outsourcer (principal) while guaranteeing correct computation. Furthermore, we incorporate practical restrictions of the maximum enforceable fine, limited and/or costly auditing, and bounded budget of the outsourcer. By examining the optimal contracts, we provide insights on how resources should be utilized when auditing capacity and enforceability are limited. Finally, we present a light-weight cryptographic implementation of the contracts and discuss a comparison across different implementations of auditing in outsourced computation

Revocation in Publicly Verifiable Outsourced Computation

The combination of software-as-a-service and the increasing use of mobile devices gives rise to a considerable difference in computational power between servers and clients. Thus, there is a desire for clients to outsource the evaluation of complex functions to an external server. Servers providing such a service may be rewarded per computation, and as such have an incentive to cheat by returning garbage rather than devoting resources and time to compute a valid result. In this work, we introduce the notion of Revocable Publicly Verifiable Computation (RPVC), where a cheating server is revoked and may not perform future computations (thus incurring a financial penalty). We introduce a Key Distribution Center (KDC) to efficiently handle the generation and distribution of the keys required to support RPVC. The KDC is an authority over entities in the system and enables revocation. We also introduce a notion of blind verification such that results are verifiable (and hence servers can be rewarded or punished) without learning the value. We present a rigorous definitional framework, define a number of new security models and present a construction of such a scheme built upon Key-Policy Attribute-based Encryption.

Secure Outsourced Computation on Encrypted Data

Homomorphic encryption (HE) is a promising cryptographic technique that supports computations on encrypted data without requiring decryption first. This ability allows sensitive data, such as genomic, financial, or location data, to be outsourced for evaluation in a resourceful third-party such as the cloud without compromising data privacy. Basic homomorphic primitives support addition and multiplication on ciphertexts. These primitives can be utilized to represent essential computations, such as logic gates, which subsequently can support more complex functions. We propose the construction of efficient cryptographic protocols as building blocks (e.g., equality, comparison, and counting) that are commonly used in data analytics and machine learning. We explore the use of these building blocks in two privacy-preserving applications. One application leverages our secure prefix matching algorithm, which builds on top of the equality operation, to process geospatial queries on encrypted locations. The other applies our secure comparison protocol to perform conditional branching in private evaluation of decision trees. There are many outsourced computations that require joint evaluation on private data owned by multiple parties. For example, Genome-Wide Association Study (GWAS) is becoming feasible because of the recent advances of genome sequencing technology. Due to the sensitivity of genomic data, this data is encrypted using different keys possessed by different data owners. Computing on ciphertexts encrypted with multiple keys is a non-trivial task. Current solutions often require a joint key setup before any computation such as in threshold HE or incur large ciphertext size (at best, grows linearly in the number of involved keys) such as in multi-key HE. We propose a hybrid approach that combines the advantages of threshold and multi-key HE to support computations on ciphertexts encrypted with different keys while vastly reducing ciphertext size. Moreover, we propose the SparkFHE framework to support large-scale secure data analytics in the Cloud. SparkFHE integrates Apache Spark with Fully HE to support secure distributed data analytics and machine learning and make two novel contributions: (1) enabling Spark to perform efficient computation on large datasets while preserving user privacy, and (2) accelerating intensive homomorphic computation through parallelization of tasks across clusters of computing nodes. To our best knowledge, SparkFHE is the first addressing these two needs simultaneously

Access Control in Publicly Verifiable Outsourced Computation

Publicly Verifiable Outsourced Computation (PVC) allows devices with restricted re-sources to delegate expensive computations to more powerful external servers, and to verify the correctness of results. Whilst highlybeneficial in many situations, this increases the visi-bility and availability of potentially sensitive data, so we may wish to limit the sets of entities that can view input data and results. Additionally, it is highly unlikely that all users have identical and uncontrolled access to all functionality within an organization. Thus there is a need for access control mechanisms in PVC environments. In this work, we define a new framework for Publicly Verifiable Outsourced Computation with Access Control (PVC-AC). We formally define algorithms to provide different PVC functionality for each entity within a large outsourced computation environment, and discuss the forms of access control policies that are applicable, and necessary, in such environments, as well as formally modelling the resulting security properties. Finally, we give an example instantiation that (in a black-box and generic fashion) combines existing PVC schemes with symmetric Key Assignment Schemes to cryptographically enforce the policies of interest.

Incentivizing Outsourced Computation

We describe different strategies a central authority, the boss, can use to distribute computation to untrusted contractors. Our problem is inspired by volunteer distributed computing projects such as SETI@home, which outsource computation to large numbers of participants. For many tasks, verifying a task\u27s output requires as much work as computing it again; additionally, some tasks may produce certain outputs with greater probability than others. A selfish contractor may try to exploit these factors, by submitting potentially incorrect results and claiming a reward. Further, malicious contractors may respond incorrectly, to cause direct harm or to create additional overhead for result-checking. We consider the scenario where there is a credit system whereby users can be rewarded for good work and fined for cheating. We show how to set rewards and fines that incentivize proper behavior from rational contractors, and mitigate the damage caused by malicious contractors. We analyze two strategies: random double-checking by the boss, and hiring multiple contractors to perform the same job. We also present a bounty mechanism when multiple contractors are employed; the key insight is to give a reward to a contractor who catches another worker cheating. Furthermore, if we can assume that at least a small fraction h of the contractors are honest (1% − 10%), then we can provide graceful degradation for the accuracy of the system and the work the boss has to perform. This is much better than the Byzantine approach, which typically assumes h > 60%

Hybrid Publicly Verifiable Computation

Publicly Verifiable Outsourced Computation (PVC) allows weak devices to delegate com-putations to more powerful servers, and to verify the correctness of results. Delegation and verification rely only on public parameters, and thus PVC lends itself to large multi-user systems where entities need not be registered. In such settings, individual user requirements may be diverse and cannot be realised with current PVC solutions. In this paper, we in-troduce Hybrid PVC (HPVC) which, with a single setup stage, provides a flexible solution to outsourced computation supporting multiple modes: (i) standard PVC, (ii) PVC with cryptographically enforced access control policies restricting the servers that may perform a given computation, and (iii) a reversed model of PVC which we call Verifiable Delegable Computation (VDC) where data is held remotely by servers. Entities may dynamically play the role of delegators or servers as required

Information-Theoretic Secure Outsourced Computation in Distributed Systems

Secure multi-party computation (secure MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. One of the earliest secure MPC primitives is the Shamir\u27s secret sharing (SSS) scheme. SSS has many advantages over other popular secure MPC primitives like garbled circuits (GC) -- it provides information-theoretic security guarantee, requires no complex long-integer operations, and often leads to more efficient protocols. Nonetheless, SSS receives less attention in the signal processing community because SSS requires a larger number of honest participants, making it prone to collusion attacks. In this dissertation, I propose an agent-based computing framework using SSS to protect privacy in distributed signal processing. There are three main contributions to this dissertation. First, the proposed computing framework is shown to be significantly more efficient than GC. Second, a novel game-theoretical framework is proposed to analyze different types of collusion attacks. Third, using the proposed game-theoretical framework, specific mechanism designs are developed to deter collusion attacks in a fully distributed manner. Specifically, for a collusion attack with known detectors, I analyze it as games between secret owners and show that the attack can be effectively deterred by an explicit retaliation mechanism. For a general attack without detectors, I expand the scope of the game to include the computing agents and provide deterrence through deceptive collusion requests. The correctness and privacy of the protocols are proved under a covert adversarial model. Our experimental results demonstrate the efficiency of SSS-based protocols and the validity of our mechanism design