Input Privacy Research Articles

On privacy preserving data release of linear dynamic networks

Distributed data sharing in dynamic networks is ubiquitous. It raises the concern that the private information of dynamic networks could be leaked when data receivers are malicious or communication channels are insecure. In this paper, we propose to intentionally perturb the inputs and outputs of a linear dynamic system to protect the privacy of target initial states and inputs from released outputs. We formulate the problem of perturbation design as an optimization problem which minimizes the cost caused by the added perturbations while maintaining system controllability and ensuring the privacy. We analyze the computational complexity of the formulated optimization problem. To minimize the ℓ0 and ℓ2 norms of the added perturbations, we derive their convex relaxations which can be efficiently solved. The efficacy of the proposed techniques is verified by a case study on a heating, ventilation, and air conditioning system.

Secure outsourcing of modular inverses and scalar multiplications on elliptic curves

Currently, pricey data analysis urges outsourcing computations, such as modular inverse and point multiplication in elliptic curve cryptosystem. However, it raises security concerns on the untrusted cloud. Thus, we propose two outsourcing protocols for these two operations, respectively. For efficiency, in the first protocol, only three modular multiplications required by the client to outsource a modular inverse operation. In the second protocol, a point multiplication operation can be carried out just by doing two addition operations on elliptic curves. For security, in both protocols, the input is randomly split into two parts to meet both input and output privacy. The security is theoretically proved under a single server and two non-colluding servers' models, respectively. Furthermore, the cheating behaviours of the servers can be detected by probabilities of 100% and 75%, respectively. Besides the achieved security goal, experiments confirm the significant improvement on efficiency in comparison with the traditional ones.

DeepPAR and DeepDPA: Privacy Preserving and Asynchronous Deep Learning for Industrial IoT

Industrial Internet of Things (IIoT) is significant of building powerful industrial systems and applications. Deep learning has provided a promising opportunity to extract useful knowledge by utilizing vast amounts of data in IIoT. However, lacking of massive public datasets will lead to low performance and overfitting of the learned model. Therefore, the federated deep learning over distributed datasets has been proposed. Whereas, it inevitably introduces some new security challenges, i.e., disclosing participant's data privacy. However, existing methods cannot guarantee each participant's data privacy in a learning group. In this article, we propose two privacy-preserving asynchronous deep learning schemes [privacy-preserving and asynchronous deep learning via re-encryption (DeepPAR) and dynamic privacy-preserving and asynchronous deep learning (DeepDPA)]. Compared to the state-of-the-art work, DeepPAR protects each participant's input privacy while preserving dynamic update secrecy inherently. Meanwhile, DeepDPA enables to guarantee backward secrecy of group participants in a lightweight manner. Security analysis and performance evaluations on real dataset show that our proposed schemes are secure, efficient, and effective.

Optimal Accuracy-Privacy Trade-Off for Secure Computations

The purpose of secure multi-party computation is to enable protocol participants to compute a public function of their private inputs while keeping their inputs secret, without resorting to any trusted third party. However, opening the public output of such computations inevitably reveals some information about the private inputs. We propose a measure generalizing both Renyi entropy and $g$ -entropy so as to quantify this information leakage. In order to control and restrain such information flows, we introduce the notion of function substitution, which replaces the computation of a function that reveals sensitive information with that of an approximate function. We exhibit theoretical bounds for the privacy gains that this approach provides and experimentally show that this enhances the confidentiality of the inputs while controlling the distortion of computed output values. Finally, we investigate the inherent compromise between accuracy of computation and privacy of inputs and we demonstrate how to realize such optimal trade-offs.

Constructing weak simulations from linear implications for processes with private names

AbstractThis paper clarifies that linear implication defines a branching-time preorder, preserved in all contexts, when used to compare embeddings of process in non-commutative logic. The logic considered is a first-order extension of the proof system BV featuring a de Morgan dual pair of nominal quantifiers, called BV1. An embedding of π-calculus processes as formulae in BV1 is defined, and the soundness of linear implication in BV1 with respect to a notion of weak simulation in the π -calculus is established. A novel contribution of this work is that we generalise the notion of a ‘left proof’ to a class of formulae sufficiently large to compare embeddings of processes, from which simulating execution steps are extracted. We illustrate the expressive power of BV1 by demonstrating that results extend to the internal π -calculus, where privacy of inputs is guaranteed. We also remark that linear implication is strictly finer than any interleaving preorder.

Cryptanalysis of Morillo–Obrador polynomial delegation schemes

Verifiable computation (VC) allows a client to outsource (delegate) the computation of a function f on an input x to a server and then verify the server's results with substantially less time than computing f(x) from scratch. The security of VC requires no efficient adversary can persuade the client to accept any wrong results. Morillo and Obrador (PST 2013) proposed three VC schemes for outsourcing the computation of polynomial functions and claimed that all schemes are secure under the decisional subgroup membership assumption. The authors show a simple attack against the security of their first scheme and then extend the attack to the other two schemes. Morillo and Obrador (PST 2013) also claimed that their third scheme keeps the client's input private under the square root assumption. The authors show that this is not true under the standard definition of input privacy. In particular, a curious server can extract the client's input x, if the x is not too large. The authors' results show that Morillo-Obrador schemes cannot be used in the polynomial delegation.

An efficient and secure ridge regression outsourcing scheme in wearable devices

Ridge regression is an important approach in many applications, such as healthcare system of smart wearable equipments. Due to the limited resources of wearable devices, outsourcing is a promising computation paradigm. Nevertheless, it also suffers from some privacy challenges, as outsourced computation probably involves some sensitive data. In this paper, we propose a ridge regression outsourcing scheme, which can securely utilize the cloud to analyse large-scale wearable device dataset and dramatically reduce the computation cost of the resource-limited clients. Technically, we use random vectors and dense matrices to perturb input dataset and regression output, such that both input privacy and output privacy can be efficiently protected. Then, we present a highly-efficient verification algorithm to robustly check the correctness of cloud’s answer against a dishonest/lazy cloud server. Finally, we evaluate our scheme through theoretical analysis and extensive experiments. The results show we can achieve input/output privacy, correctness, robust checkability and practical efficiency.

Cryptographic collusion-resistant protocols for secure sum

Secure summation is one of the most applicable functions of secure multiparty computation MPC in which a group of users securely computes the summation value of their private inputs. The current solutions to this problem are basically on adding a random number to private inputs or splitting the inputs among users which need secure channel among members. Moreover, to be resistant against collusion of n − 2 players, they impose high communication cost. In this paper, we propose three cryptography-based protocols for secure sum that do not need secure channel and are secure against collusion of n − 2 players. Also, the communication cost of the proposed protocols is of complexity On. Based on the privacy requirements, the proposed protocols can provide the final result privacy as well as the private input privacy.

Cryptographic collusion-resistant protocols for secure sum

Secure summation is one of the most applicable functions of secure multiparty computation (MPC) in which a group of users securely computes the summation value of their private inputs. The current solutions to this problem are basically on adding a random number to private inputs or splitting the inputs among users which need secure channel among members. Moreover, to be resistant against collusion of n − 2 players, they impose high communication cost. In this paper, we propose three cryptography-based protocols for secure sum that do not need secure channel and are secure against collusion of n − 2 players. Also, the communication cost of the proposed protocols is of complexity O(n). Based on the privacy requirements, the proposed protocols can provide the final result privacy as well as the private input privacy.

An efficient privacy-preserving P2P protocol for computing maximum value in the presence of active adversaries

Joint and secure computation of the private data inputs of a group of users is an interesting problem in current P2P applications. The original problem of this type is the Millionaires’ Problem, in which two millionaires wish to know who is richer without disclosing their wealth. In this paper, we study the general case of the Millionaires’ Problem, in which a group of users try to compute the greatest value among their private inputs. We propose two solutions to address this problem. The first solution, which we call Smax-SH, is based on the AV-net protocol. It computes the greatest value while preserving the private input privacy in the semi-honest model. The second solution, called Smax-M, computes the maximum private value in the malicious model. The Smax-M protocol applies a zero-knowledge proof for security from malicious participants and active adversaries. We discuss the performance and security analysis of the proposed protocols and show that the each is efficient in terms of computation and communication costs. We also show that the Smax-M protocol is secure against a partial collusion attack in a malicious model.

Strongly average‐case secure obfuscation: achieving input privacy and circuit obscurity

AbstractA program obfuscator is a compiling algorithm that takes a program/circuit as input and generates a new garbled circuit to implement the same functionality as before while obtaining hard‐to‐understand in some sense, that is, infeasible to learn information from the garbled circuit. In order to obtain a practical application, an obfuscation should satisfy equivalent in functionality, polynomial slowdown in efficiency, and virtual black‐box in security. In this paper, we model a stronger cryptographic obfuscation that does not only achieves the obfuscated circuit obscurity but also supports input re‐key privacy. In order to implement the re‐encryption obfuscation, we at first propose a key‐privacy two‐level encryption mechanism that implicitly supports the transformation from level‐2 ciphertext into level‐1 one, which provides an efficient method to re‐encrypt the ciphertext without explicitly decryption procedure. Under the mechanism of two‐level encryption and function of re‐encryption, we construct an obfuscation of re‐encryption that takes as input a probabilistic (keys) circuit and outputs a transformed circuit. We also give the proof that the obfuscator achieves the average‐case security for the circuit family under the extended DBDH assumption and Decisional Linear assumption in the standard model. Copyright © 2016 John Wiley & Sons, Ltd.

PPDM: A Privacy-Preserving Protocol for Cloud-Assisted e-Healthcare Systems

E-healthcare systems have been increasingly facilitating health condition monitoring, disease modeling and early intervention, and evidence-based medical treatment by medical text mining and image feature extraction. Owing to the resource constraint of wearable mobile devices, it is required to outsource the frequently collected personal health information (PHI) into the cloud. Unfortunately, delegating both storage and computation to the untrusted entity would bring a series of security and privacy issues. The existing work mainly focused on fine-grained privacy-preserving static medical text access and analysis, which can hardly afford the dynamic health condition fluctuation and medical image analysis. In this paper, a secure and efficient privacy-preserving dynamic medical text mining and image feature extraction scheme PPDM in cloud-assisted e-healthcare systems is proposed. Firstly, an efficient privacy-preserving fully homomorphic data aggregation is proposed, which serves the basis for our proposed PPDM. Then, an outsourced disease modeling and early intervention is achieved, respectively by devising an efficient privacy-preserving function correlation matching PPDM1 from dynamic medical text mining and designing a privacy-preserving medical image feature extraction PPDM2. Finally, the formal security proof and extensive performance evaluation demonstrate our proposed PPDM achieves a higher security level (i.e., information-theoretic security for input privacy and adaptive chosen ciphertext attack (CCA2) security for output privacy) in the honest but curious model with optimized efficiency advantage over the state-of-the-art in terms of both computational and communication overhead.

Efficient Privacy-Preserving Facial Expression Classification

This paper proposes an efficient algorithm to perform privacy-preserving (PP) facial expression classification (FEC) in the client-server model. The server holds a database and offers the classification service to the clients. The client uses the service to classify the facial expression (FaE) of subject. It should be noted that the client and server are mutually untrusted parties and they want to perform the classification without revealing their inputs to each other. In contrast to the existing works, which rely on computationally expensive cryptographic operations, this paper proposes a lightweight algorithm based on the randomization technique. The proposed algorithm is validated using the widely used JAFFE and MUG FaE databases. Experimental results demonstrate that the proposed algorithm does not degrade the performance compared to existing works. However, it preserves the privacy of inputs while improving the computational complexity by <inline-formula><tex-math notation="LaTeX">$120$</tex-math></inline-formula> times and communication complexity by <inline-formula><tex-math notation="LaTeX">$31$</tex-math></inline-formula> percent against the existing homomorphic cryptography based approach.

Secure computation under network and physical attacks

Secure computation enables many parties to jointly compute a function of their private inputs. The security requirement is that the input privacy of any honest party is preserved even if other parties participating in the protocol collude or deviate from the protocol. In concurrent and physical adversarial parties try to break the privacy of honest parties by exploiting the network connection or physical weaknesses of the honest parties’ machine. This article provides an overview of the results for achieving secure computation in presence of concurrent and physical attacks contained in the phD thesis:”Secure Computation under concurrent and physical attacks, with emphasis to the relation of such results with the state of the art.

Behavioral Identification of Trusted Third Party in Secure Multiparty Computing Protocol

We present a solution for identification and reduction of malicious conduct by Trusted Third parties (TTPs) in Secure Multiparty Computing Protocol. This paper also proposes a secured protocol for computation and defines encryption to be performed before sending inputs for computation. Our protocol uses eenvelopes for sharing keys between parties and TTPs. This key sharing is done on the basis of RSA algorithm. This ensures that parties send their data in encrypted manner to TTPs in order to maintain privacy and security of inputs. Also, single and multi trusted third party model is compared and the probabilistic evidences for them have been analyzed with security analysis graphs.

Secure Two-Party Computation via Cut-and-Choose Oblivious Transfer

Protocols for secure two-party computation enable a pair of parties to compute a function of their inputs while preserving security properties such as privacy, correctness and independence of inputs. Recently, a number of protocols have been proposed for the efficient construction of two-party computation secure in the presence of malicious adversaries (where security is proven under the standard simulation-based ideal/real model paradigm for defining security). In this paper, we present a protocol for this task that follows the methodology of using cut-and-choose to boost Yao’s protocol to be secure in the presence of malicious adversaries. Relying on specific assumptions (DDH), we construct a protocol that is significantly more efficient and far simpler than the protocol of Lindell and Pinkas (Eurocrypt 2007) that follows the same methodology. We provide an exact, concrete analysis of the efficiency of our scheme and demonstrate that (at least for not very small circuits) our protocol is more efficient than any other known today.

Advances and impact of secure function evaluation

Secure function evaluation (SFE) is an area of cryptography concerned with the design of algorithms that allow mutually distrustful partners to evaluate desired functions on their inputs, while maximally preserving input privacy. With faster computers and networks, and continuous algorithm improvements, SFE has become within reach of practicality. The first large-scale industrial application of SFE—the result of over 20 years of relevant research—has been successfully completed in Denmark, where SFE was used to run a country-wide secure sugar beets auction. In this paper, we will provide a brief overview of SFE, the problem it solves, and its uses. We will describe several recent research advancements and argue that SFE is “the next big thing.”

Advanced Approach in Sensitive Rule Hiding

Privacy preserving data mining is a novel research direction in data mining and statistical databases, which has recently been proposed in response to the concerns of preserving personal or sensible information derived from data mining algorithms. There have been two types of privacy proposed concerning data mining. The first type of privacy, called output privacy, is that the data is altered so that the mining result will preserve certain privacy. The second type of privacy, called input privacy, is that the data is manipulated so that the mining result is not affected or minimally affected. For output privacy in hiding association rules, current approaches require hidden rules or patterns to be given in advance. However, to specify hidden rules, entire data mining process needs to be executed. For some applications, only certain sensitive rules that contain sensitive items are required to hide. In this work, an algorithm ISSRH (Increase Support Sensitive Rule Hiding) is proposed, to hide the sensitive rules that contain sensitive items, so that sensitive rules containing specified sensitive items on the right hand side of the rule cannot be inferred through association rule mining. Example illustrating the proposed approach is given. The characteristics of the algorithm are discussed.

A New Approach to Sensitive Rule Hiding

Privacy preserving data mining is a novel research direction in data mining and statistical databases, which has recently been proposed in response to the concerns of preserving personal or sensible information derived from data mining algorithms. There have been two types of privacy proposed concerning data mining. The first type of privacy, called output privacy, is that the data is altered so that the mining result will preserve certain privacy. The second type of privacy, called input privacy, is that the data is manipulated so that the mining result is not affected or minimally affected. For output privacy in hiding association rules, current approaches require hidden rules or patterns to be given in advance. However, to specify hidden rules, entire data mining process needs to be executed. For some applications, only certain sensitive rules that contain sensitive items are required to hide. In this work, an algorithm SRH (Sensitive Rule Hiding) is proposed, to hide the sensitive rules that contain sensitive items, so that sensitive rules containing specified sensitive items on the right hand side of the rule cannot be inferred through association rule mining. Example illustrating the proposed approach is given. The characteristics of the algorithm are discussed.

Identification of Sensitive Items in Privacy Preserving - Association Rule Mining

The concept of Privacy-Preserving has recently been proposed in response to the concerns of preserving personal or sensible information derived from data mining algorithms. For example, through data mining, sensible information such as private information or patterns may be inferred from non-sensible information or unclassified data. As large repositories of data contain confidential rules that must be protected before published, association rule hiding becomes one of important privacy preserving data mining problems. There have been two types of privacy concerning data mining. Output privacy tries to hide the mining results by minimally altering the data. Input privacy tries to manipulate the data so that the mining result is not affected or minimally affected. For some applications certain sensitive predictive rules are hidden that contain given sensitive items. To identify the sensitive items an algorithm SENSIDENT is proposed. The results of the work have been given.