PACAS: Privacy-Aware, Data Cleaning-as-a-Service

Yu, Haitao; Milani, Mostafa; Chiang, Fei

doi:10.1109/bigdata.2018.8622249

Cited by 11 publications

(18 citation statements)

References 15 publications

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“…Recently, the privacy-aware data cleaning techniques have significantly reduced the data preparation cost of data analysis pipeline [278], [279]. These techniques allow the clients to buy clean, and curated data from heterogeneous service provider to perform analytics without compromising user's privacy.…”

Section: Summary and Discussion About The Privacy Issues In Futurmentioning

confidence: 99%

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

2021

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Section: Summary and Discussion About The Privacy Issues In Futurmentioning

confidence: 99%

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

2021

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“…EDCleaner proposed by Wang et al [125] is designed for social network data, detection and cleaning are performed through the characteristics of statistical data fields. Huang et al [126] propose PACAS which is a framework for data cleaning between service providers and customers. Huang et al [127] present TsOutlier, a new framework for detecting outliers with explanations over IoT data.…”

Section: A Toolsmentioning

confidence: 99%

Time Series Data Cleaning: A Survey

Wang

2020

IEEE Access

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Errors are prevalent in time series data, which is particularly common in the industrial field. Data with errors could not be stored in the database, which results in the loss of data assets. At present, to deal with these time series containing errors, besides keeping original erroneous data, discarding erroneous data and manually checking erroneous data, we can also use the cleaning algorithm widely used in the database to automatically clean the time series data. This survey provides a classification of time series data cleaning techniques and comprehensively reviews the state-of-the-art methods of each type. Besides we summarize data cleaning tools, systems and evaluation criteria from research and industry. Finally, we highlight possible directions time series data cleaning. INDEX TERMS Data cleaning, data quality, time series.

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“…For example, upon closer inspection of Table 3, the values in the QI attributes in records g 1 − g 3 are associated with the same family of medication, since ibuprofen, addaprin, and naproxen all belong to the the NSAID class, which are analgesic drugs. In past work, we introduced a privacypreserving framework that uses an extension of (X,Y)-anonymity to incorporate a generalization hierarchy, which capture the semantics of an attribute domain 1 [12]. In Table 3, the medications in g 1 − g 3 are modeled as synonyms in such a hierarchy.…”

Section: Introductionmentioning

confidence: 99%

“…In Table 3, the medications in g 1 − g 3 are modeled as synonyms in such a hierarchy. In this paper, we extend our earlier work [12] to define a semantic distance metric between values in a generalization hierarchy, and to incorporate generalized values as part of the data repair process.…”

Section: Introductionmentioning

confidence: 99%

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Privacy-Aware Data Cleaning-as-a-Service (Extended Version)

Huang,

Milani,

Chiang

2020

Preprint

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Data cleaning is a pervasive problem for organizations as they try to reap value from their data. Recent advances in networking and cloud computing technology have fueled a new computing paradigm called Database-as-a-Service, where data management tasks are outsourced to large service providers. In this paper, we consider a Data Cleaning-as-a-Service model that allows a client to interact with a data cleaning provider who hosts curated, and sensitive data. We present PACAS: a Privacy-Aware data Cleaning-As-a-Service model that facilitates interaction between the parties with client query requests for data, and a service provider using a data pricing scheme that computes prices according to data sensitivity. We propose new extensions to the model to define generalized data repairs that obfuscate sensitive data to allow data sharing between the client and service provider. We present a new semantic distance measure to quantify the utility of such repairs, and we re-define the notion of consistency in the presence of generalized values. The PACAS model uses (X,Y,L)-anonymity that extends existing data publishing techniques to consider the semantics in the data while protecting sensitive values. Our evaluation over real data show that PACAS safeguards semantically related sensitive values, and provides lower repair errors compared to existing privacy-aware cleaning techniques.

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PACAS: Privacy-Aware, Data Cleaning-as-a-Service

Cited by 11 publications

References 15 publications

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Time Series Data Cleaning: A Survey

Privacy-Aware Data Cleaning-as-a-Service (Extended Version)

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