GraphShield: Dynamic Large Graphs for Secure Queries with Forward Privacy

Du, Minxin; Shuangke, Wu; Wang, Qian; Chen, Dian; Jiang, Peipei; Mohaisen, Aziz

doi:10.1109/tkde.2020.3024883

Cited by 20 publications

(8 citation statements)

References 45 publications

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“…The work [27] focuses on GNNs over decentralized spatio-temporal data, and has the clients exchange model updates with the server in cleartext. In contrast, the works [28], [29] focus on graph datasets and design There has been a line of work [31][32][33][34][35] aimed at the support for graph analytics with cryptographic methods like secure multi-party computation techniques and searchable encryption. The main focus of this line of work has been on supporting different kinds of graph queries under different scenarios in a secure manner.…”

Section: Privacy-preserving Graph Analyticsmentioning

confidence: 99%

“…In contrast, the works [34], [35] focus on the support for privacy-preserving shortest path queries in an outsourcing setting, where private queries need to be executed over encrypted graphs outsourced to the cloud. The work [32] designs protocols that can support privacypreserving shortest distance queries and maximum flow queries over outsourced encrypted graphs. These works rely on the combination of searchable encryption (e.g., orderrevealing encryption), homomorphic encryption, and/or garbled circuits.…”

Section: Privacy-preserving Graph Analyticsmentioning

confidence: 99%

“…In PrivGED, the power of the cloud is split into two cloud servers from different trust domains which can be hosted by different service providers in practice. Such multi-server model is getting increasingly popular in recent years for security designs in various domains, including both academia [32], [41][42][43][44][45][46][47] and industry [48], [49]. The adoption of such model in PrivGED follows this trend.…”

Section: Architecturementioning

confidence: 99%

See 2 more Smart Citations

Privacy-Preserving Analytics on Decentralized Social Graphs: The Case of Eigendecomposition

Wang¹,

Zheng²,

Jia³

et al. 2022

Preprint

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Analytics over social graphs allows to extract valuable knowledge and insights for many fields like community detection, fraud detection, and interest mining. In practice, decentralized social graphs frequently arise, where the social graph is not available to a single entity and is decentralized among a large number of users, each holding only a limited local view about the whole graph. Collecting the local views for analytics of decentralized social graphs raises critical privacy concerns, as they encode private information about the social interactions among individuals. In this paper, we design, implement, and evaluate PrivGED, a new system aimed at privacy-preserving analytics over decentralized social graphs. PrivGED focuses on the support for eigendecomposition, one popular and fundamental graph analytics task producing eigenvalues/eigenvectors over the adjacency matrix of a social graph and benefits various practical applications. PrivGED is built from a delicate synergy of insights on graph analytics, lightweight cryptography, and differential privacy, allowing users to securely contribute their local views on a decentralized social graph for a cloud-based eigendecomposition analytics service while gaining strong privacy protection. Extensive experiments over real-world social graph datasets demonstrate that PrivGED achieves accuracy comparable to the plaintext domain, with practically affordable performance superior to prior art.

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Section: Privacy-preserving Graph Analyticsmentioning

confidence: 99%

Section: Privacy-preserving Graph Analyticsmentioning

confidence: 99%

Section: Architecturementioning

confidence: 99%

See 1 more Smart Citation

Privacy-Preserving Analytics on Decentralized Social Graphs: The Case of Eigendecomposition

Wang¹,

Zheng²,

Jia³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…After that, Niu et al [18] further proposed attribute-based encryption (ABE), which is based searchable encryption to achieve data sharing. Du et al [19] proposed a structured encryption scheme for graphs that can support other classic graph-based queries and more complicated analytics. Salmani et al [20] considered eavesdropping attack and replay attack and designed a forward security scheme with lower time complexity of deletion operation.…”

Section: Related Workmentioning

confidence: 99%

AFFIRM: Provably Forward Privacy for Searchable Encryption in Cooperative Intelligent Transportation System

Wang

Chen

Shojafar

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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With the construction of intelligent transportation, big data with heterogeneous, multi-source and massive characteristics has become an important carrier of cooperative intelligent transportation systems (C-ITS) and plays an important role. Big data in C-ITS can break through the restrictions between regions and entities and then learning cooperatively by sharing data. In addition, the combined efficiency and information integration advantages of big data are conducive to the construction of a comprehensive and three-dimensional traffic information system and can enhance traffic prediction. However, such substantial sensitive data, mainly on the cloud infrastructure, exposes several vulnerabilities like data leakages and privacy breaks, especially when data is shared for cooperative learning purposes. To address this, this paper proposes a forward privacy-preserving scheme, named AFFIRM, for multi-party encrypted sample alignment adopting cooperative learning in C-ITS. By introducing the searchable encryption method, we realize the sample alignment of cooperative learning in the multi-party encrypted data space. AFFIRM ensures encrypted sample alignment under the condition of forward privacy security. We have formally proved that the proposed scheme satisfies both forward security and validity. We have assessed AFFIRM by validating the potential threat of malicious tampering by privacy attackers and malicious personnel search for the aligned sample data and verify it. Finally, we numerically tested and compared AFFIRM against the corresponding ones of some state-of-the-art schemes under various record sizes, servers and processing.

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“…There are potentials to combine more functional SSE techniques, such as tree-based range search [115], [188], [189] and Boolean search [190]- [192]. Interestingly, the structured encryption [193] can encrypt and query the structured data, such as graphs, which may be adopted to solve the secure routing problem, such as the shortest distant computation [194]- [196].…”

Section: A Limitations Of the Cryptographic Toolsmentioning

confidence: 99%